A B C D E F G H I J K L M N O P Q R S T U V W Z 

A

adapt(Evidence, OnlineLearningOptions) - Method in class com.bayesserver.learning.parameters.OnlineLearning
Adapt the parameters of a Bayesian network using Bayesian statistics.
add(int, CustomProperty) - Method in class com.bayesserver.CustomPropertyCollection
add(String, Class) - Method in class com.bayesserver.data.DataColumnCollection
Adds a new DataColumn to the collection.
add(int, DataColumn) - Method in class com.bayesserver.data.DataColumnCollection
Adds a DataColumn instance at the given index.
add(Object...) - Method in class com.bayesserver.data.DataRowCollection
Adds a new row of values to the collection.
add(int, DataRow) - Method in class com.bayesserver.data.DataRowCollection
Adds a DataRow instance at the given index.
add(int, Variable) - Method in class com.bayesserver.data.sampling.ExcludedVariables
 
add(int, QueryDistribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
add(Distribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
Adds the specified distribution, automatically creating a QueryDistribution instance.
add(Distribution) - Method in interface com.bayesserver.inference.QueryDistributionCollection
Adds the specified distribution, automatically creating a QueryDistribution instance.
add(int, LinkConstraint) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
 
add(int, Link) - Method in class com.bayesserver.NetworkLinkCollection
Inserts an element into the collection at the specified index.
add(int, Node) - Method in class com.bayesserver.NetworkNodeCollection
Inserts an element into the collection at the specified index.
add(int, NodeGroup) - Method in class com.bayesserver.NetworkNodeGroupCollection
add(int, String) - Method in class com.bayesserver.NodeGroupCollection
Inserts an element into the collection at the specified index.
add(int, Variable) - Method in class com.bayesserver.NodeVariableCollection
Inserts an element into the collection at the specified index.
add(int, State) - Method in class com.bayesserver.StateCollection
Inserts an element into the collection at the specified index.
add(Table) - Method in class com.bayesserver.Table
Adds the values from another table into this instance.
addAll(double) - Method in class com.bayesserver.Table
Adds the specified value onto all table elements.
addMonitor(NetworkMonitor) - Method in class com.bayesserver.Network
For internal use only.
ArcReversal - Class in com.bayesserver
Contains methods to reverse the direction of a Link, known as arc reversal.
areAllValuesNonZero() - Method in class com.bayesserver.Table
Returns true if none of the values in the Table equal zero, or false otherwise.
Association - Class in com.bayesserver.analysis
Calculates the strength between pairs of variables or sets of variables.
AssociationOptions - Class in com.bayesserver.analysis
Options that affect the link strength algorithm.
AssociationOptions() - Constructor for class com.bayesserver.analysis.AssociationOptions
 
AssociationOutput - Class in com.bayesserver.analysis
Contains the results of an Association analysis.
AssociationPair - Class in com.bayesserver.analysis
Defines two sets of variables to be analyzed by the Association algorithm.
AssociationPair(Variable, Variable) - Constructor for class com.bayesserver.analysis.AssociationPair
Initializes a new instance of the AssociationPair class with individual variables.
AssociationPair(Node, Node) - Constructor for class com.bayesserver.analysis.AssociationPair
Initializes a new instance of the AssociationPair class with individual nodes.
AssociationPair(List<VariableContext>, List<VariableContext>) - Constructor for class com.bayesserver.analysis.AssociationPair
Initializes a new instance of the AssociationPair class with two sets of variable contexts.
AssociationPairOutput - Class in com.bayesserver.analysis
Contains the results of the association calculations between two sets of variables.
AutoInsight - Class in com.bayesserver.analysis
Uses comparison queries to automatically derive insight about a target variable from a trained network.
AutoInsightJSDivergence - Enum in com.bayesserver.analysis
Determines the type of Jensen Shannon divergence calculations, if any, performed during an auto insight analysis.
AutoInsightKLDivergence - Enum in com.bayesserver.analysis
Determines the type of KL divergence calculations, if any, performed during an auto insight analysis.
AutoInsightOptions - Class in com.bayesserver.analysis
Options that affect auto-insight calculations.
AutoInsightOptions() - Constructor for class com.bayesserver.analysis.AutoInsightOptions
 
AutoInsightOutput - Class in com.bayesserver.analysis
Contains the results obtained from AutoInsight.
AutoInsightSamplingOptions - Class in com.bayesserver.analysis
Options that affect any sampling required during auto-insight calculations.
AutoInsightSamplingOptions() - Constructor for class com.bayesserver.analysis.AutoInsightSamplingOptions
 
AutoInsightStateOutput - Class in com.bayesserver.analysis
Contains the results obtained from AutoInsight for each test variable.
AutoInsightStateOutputCollection - Class in com.bayesserver.analysis
Represents a collection of AutoInsightStateOutput instances.
AutoInsightVariableOutput - Class in com.bayesserver.analysis
Represents the output obtained from AutoInsight for a test variable.
AutoInsightVariableOutputCollection - Class in com.bayesserver.analysis
Represents a collection of AutoInsightVariableOutput instances.

B

beginUpdate() - Method in class com.bayesserver.inference.DefaultEvidence
Disables change notifications (if present), until Evidence.endUpdate() is called.
beginUpdate() - Method in interface com.bayesserver.inference.Evidence
Disables change notifications (if present), until Evidence.endUpdate() is called.
Bounds - Class in com.bayesserver
Stores the position and size of an element.
Bounds(double, double, double, double) - Constructor for class com.bayesserver.Bounds
Initializes a new instance of the Bounds class.

C

calculate(List<AssociationPair>, Evidence, AssociationOptions) - Static method in class com.bayesserver.analysis.Association
Calculates the association/information between two sets of variables, such as those at either end of a Link.
calculate(State, List<Variable>, InferenceFactory) - Static method in class com.bayesserver.analysis.AutoInsight
Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(State, List<Variable>, InferenceFactory, Evidence) - Static method in class com.bayesserver.analysis.AutoInsight
Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(Variable, List<Interval<Double>>, List<Variable>, Evidence, AutoInsightOptions) - Static method in class com.bayesserver.analysis.AutoInsight
Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(State, List<Variable>, Evidence, AutoInsightOptions) - Static method in class com.bayesserver.analysis.AutoInsight
Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(Network, List<Node>, List<Node>, Evidence, DSeparationOptions) - Static method in class com.bayesserver.analysis.DSeparation
Calculates whether sets of nodes are D-Separated, given any evidence.
calculate(Network, List<Node>, List<Integer>, List<Node>, List<Integer>, Evidence, DSeparationOptions) - Static method in class com.bayesserver.analysis.DSeparation
Calculates whether sets of nodes are D-Separated, given any evidence, and associated times for any temporal nodes.
calculate(Network, Variable, State, Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
calculate(Network, Variable, Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
calculate(Network, Distribution, Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
calculate(Network, Distribution, StateContext[], Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
calculate(Network, Evidence, List<Variable>, LogLikelihoodAnalysisOptions) - Static method in class com.bayesserver.analysis.LogLikelihoodAnalysis
Analyzes the log-likelihood based on subsets of evidence.
calculate(Variable, List<Variable>, Evidence, InferenceFactory, ValueOfInformationOptions) - Static method in class com.bayesserver.analysis.ValueOfInformation
Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
calculate(VariableContext, List<VariableContext>, Evidence, InferenceFactory, ValueOfInformationOptions) - Static method in class com.bayesserver.analysis.ValueOfInformation
Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
calculate(Distribution, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy
Measures the uncertainty of a distribution.
calculate(Distribution, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy
Measures the uncertainty of a distribution conditional on one or more variables.
calculate(Table, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy
Measures the uncertainty of a distribution conditional on one or more variables.
calculate(Table, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy
Measures the uncertainty of a distribution.
calculate(CLGaussian, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy
Measures the uncertainty of a distribution conditional on one or more variables.
calculate(CLGaussian, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy
Measures the uncertainty of a distribution.
calculate(Distribution, VariableContext, VariableContext, LogarithmBase) - Static method in class com.bayesserver.statistics.MutualInformation
Measures the dependence between two variables.
calculate(Distribution, VariableContext, VariableContext, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.MutualInformation
Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
calculate(Distribution, List<VariableContext>, List<VariableContext>, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.MutualInformation
Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
calculateStreamed(Network, Distribution, Evidence, List<Variable>, ImpactAction, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
calculateStreamed(Network, Distribution, StateContext[], Evidence, List<Variable>, ImpactAction, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
calculateStreamed(Network, Evidence, List<Variable>, LogLikelihoodAnalysisAction, LogLikelihoodAnalysisOptions) - Static method in class com.bayesserver.analysis.LogLikelihoodAnalysis
Analyzes the log-likelihood based on subsets of evidence.
Cancellation - Interface in com.bayesserver
Interface for cancelling long running operations.
canUpdate(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions
Determines whether the distribution at the specified temporal order can be updated.
canUpdate(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
Determines whether the distribution at the specified temporal order can be updated.
cdf(double) - Method in interface com.bayesserver.analysis.EmpiricalDensity
Calculates an approximate value for the cumulative distribution function (cdf).
cdf(double) - Method in class com.bayesserver.analysis.HistogramDensity
Calculates an approximate value for cdf(x).
ChowLiuLinkOutput - Class in com.bayesserver.learning.structure
Contains information about a new link learnt using the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning algorithm.
ChowLiuStructuralLearning - Class in com.bayesserver.learning.structure
A structural learning algorithm for Bayesian networks based on the Chow-Liu algorithm.
ChowLiuStructuralLearning() - Constructor for class com.bayesserver.learning.structure.ChowLiuStructuralLearning
 
ChowLiuStructuralLearningOptions - Class in com.bayesserver.learning.structure
Options for structural learning with the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning class.
ChowLiuStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
 
ChowLiuStructuralLearningOutput - Class in com.bayesserver.learning.structure
Contains information returned from the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning algorithm.
ChowLiuStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
Progress information returned from the Chow-Liu structural learning algorithm.
clear() - Method in class com.bayesserver.CustomPropertyCollection
clear() - Method in class com.bayesserver.data.DataColumnCollection
Removes all columns for the collection.
clear() - Method in class com.bayesserver.data.DataRowCollection
Removes all the rows from the collection.
clear() - Method in class com.bayesserver.data.sampling.ExcludedVariables
clear(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Clears evidence on a variable at the specified time.
clear(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Clears evidence on a node's single variable.
clear(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
Clears any evidence on a variable.
clear(Node) - Method in class com.bayesserver.inference.DefaultEvidence
Clears evidence on a node's variables.
clear() - Method in class com.bayesserver.inference.DefaultEvidence
Clears any evidence on all variables.
clear() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
clear() - Method in interface com.bayesserver.inference.Evidence
Clears any evidence on all variables, and resets the Evidence.getWeight() to 1.
clear(Variable) - Method in interface com.bayesserver.inference.Evidence
Clears evidence on a variable.
clear(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
Clears evidence on a variable at the specified time.
clear(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
Clears evidence on a node's single variable.
clear(Node) - Method in interface com.bayesserver.inference.Evidence
Clears evidence on a node's variables.
clear() - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
 
clear() - Method in class com.bayesserver.NetworkLinkCollection
Removes all elements from the collection.
clear() - Method in class com.bayesserver.NetworkNodeCollection
Removes all elements from the collection.
clear() - Method in class com.bayesserver.NetworkNodeGroupCollection
clear() - Method in class com.bayesserver.NodeGroupCollection
Removes all elements from the collection.
clear() - Method in class com.bayesserver.NodeVariableCollection
Removes all elements from the collection.
clear() - Method in class com.bayesserver.StateCollection
CLGaussian - Class in com.bayesserver
Represents a Conditional Linear Gaussian probability distribution.
CLGaussian(List<VariableContext>) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts].
CLGaussian(List<VariableContext>, HeadTail) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts].
CLGaussian(VariableContext[]) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(VariableContext[], int) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(VariableContext[], int, HeadTail) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(Node, Integer) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the variables of a single node at the specified time.
CLGaussian(List<Variable>, Integer) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the specified variables at a particular time.
CLGaussian(List<Variable>, Integer, HeadTail) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the specified variables.
CLGaussian(Variable[]) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the specified variables.
CLGaussian(Node) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with the variables of a single node.
CLGaussian(Variable) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with a single variable.
CLGaussian(VariableContext) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class from a single VariableContext.
CLGaussian(Variable, Integer) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class with a single variable at the specified time.
CLGaussian(CLGaussian) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class, copying the source distribution.
CLGaussian(CLGaussian, Integer) - Constructor for class com.bayesserver.CLGaussian
Initializes a new instance of the CLGaussian class, copying the source distribution but shifting any times by the specified number of units.
close() - Method in interface com.bayesserver.data.DataReader
Close the reader and any associated resources, such as database connections or files.
close() - Method in class com.bayesserver.data.DataReaderFiltered
 
close() - Method in class com.bayesserver.data.DataTableReader
 
close() - Method in class com.bayesserver.data.DefaultEvidenceReader
Closes any resources associated with the data such as database connections, files etc...
close() - Method in interface com.bayesserver.data.EvidenceReader
Closes any resources associated with the data such as database connections, files etc...
close() - Method in class com.bayesserver.data.timeseries.WindowDataReader
Close the reader and any associated resources, such as database connections or files.
ClusterCount - Class in com.bayesserver.analysis
Methods to determine the number of clusters (discrete states of a latent variable).
ClusterCountActions - Interface in com.bayesserver.analysis
Actions which the caller must implement to use ClusterCount.
ClusterCountOptions - Class in com.bayesserver.analysis
Options used by ClusterCount.
ClusterCountOptions() - Constructor for class com.bayesserver.analysis.ClusterCountOptions
 
ClusterCountOutput - Class in com.bayesserver.analysis
Output information returned from ClusterCount.
Clustering - Class in com.bayesserver.data.discovery
Discretizes continuous data in bins, using a probabilistic clustering algorithm.
Clustering() - Constructor for class com.bayesserver.data.discovery.Clustering
 
ClusteringLinkOutput - Class in com.bayesserver.learning.structure
Contains information about a new link learnt using the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning algorithm.
ClusteringStructuralLearning - Class in com.bayesserver.learning.structure
A structural learning algorithm for a cluster model (a.k.a mixture model).
ClusteringStructuralLearning() - Constructor for class com.bayesserver.learning.structure.ClusteringStructuralLearning
 
ClusteringStructuralLearningOptions - Class in com.bayesserver.learning.structure
Options for structural learning with the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning class.
ClusteringStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
 
ClusteringStructuralLearningOutput - Class in com.bayesserver.learning.structure
Contains information returned from the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning algorithm.
ClusteringStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
Progress information returned from the Clustering structural learning algorithm.
ClusterScore - Class in com.bayesserver.analysis
Contains the results of a cluster configuration returned from ClusterCount.
CollectionAction - Enum in com.bayesserver
Specifies how the collection is changed.
ColumnValueType - Enum in com.bayesserver.data
Specifies the type of data in a column.
com.bayesserver - package com.bayesserver
 
com.bayesserver.analysis - package com.bayesserver.analysis
 
com.bayesserver.data - package com.bayesserver.data
 
com.bayesserver.data.discovery - package com.bayesserver.data.discovery
 
com.bayesserver.data.distributed - package com.bayesserver.data.distributed
 
com.bayesserver.data.sampling - package com.bayesserver.data.sampling
 
com.bayesserver.data.timeseries - package com.bayesserver.data.timeseries
 
com.bayesserver.inference - package com.bayesserver.inference
 
com.bayesserver.learning.parameters - package com.bayesserver.learning.parameters
 
com.bayesserver.learning.structure - package com.bayesserver.learning.structure
 
com.bayesserver.statistics - package com.bayesserver.statistics
 
CombinationAction - Interface in com.bayesserver.analysis
CombinationOptions - Class in com.bayesserver.analysis
Determines which combinations are generated by Combinations.
CombinationOptions() - Constructor for class com.bayesserver.analysis.CombinationOptions
 
Combinations - Class in com.bayesserver.analysis
Generates the available state combinations for a set of variables or counts.
combine(Iterable<CrossValidationTestResult>, CrossValidationCombineMethod) - Static method in class com.bayesserver.data.CrossValidation
Provides standard ways of combining numeric test results from a number of partitions.
combine(int, CrossValidationTestResult[]) - Method in interface com.bayesserver.data.CrossValidationActions
A user supplied function to combine the test results over multiple partitioning.
compareTo(T) - Method in class com.bayesserver.Interval
compareTo(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributionKey
compareTo(Variable) - Method in class com.bayesserver.Variable
ConfusionMatrix - Class in com.bayesserver.analysis
Calculates a confusion matrix for a network which is used to predict discrete values (classification).
ConfusionMatrixCell - Class in com.bayesserver.analysis
Contains statistics about a cell in a ConfusionMatrix.
ConfusionMatrixCell() - Constructor for class com.bayesserver.analysis.ConfusionMatrixCell
 
ConstraintNotSatisfiedException - Exception in com.bayesserver.analysis
Exception raised when parameter tuning attempts to solve for a constraint that cannot be satisfied by the change(s) in parameters.
ConstraintNotSatisfiedException() - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException
Initializes a new instance of the ConstraintNotSatisfiedException class.
ConstraintNotSatisfiedException(String) - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException
Initializes a new instance of the ConstraintNotSatisfiedException class.
ConstraintNotSatisfiedException(String, Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException
Initializes a new instance of the ConstraintNotSatisfiedException class with a specified error message and a reference to the inner exception that is the cause of this exception.
ConstraintNotSatisfiedException(Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException
Initializes a new instance of the ConstraintNotSatisfiedException class a reference to the inner exception that is the cause of this exception.
ConstraintSatisfiedException - Exception in com.bayesserver.analysis
Exception raised when parameter tuning attempts to solve for a constraint that is already true.
ConstraintSatisfiedException() - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException
Initializes a new instance of the ConstraintSatisfiedException class.
ConstraintSatisfiedException(String) - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException
Initializes a new instance of the ConstraintSatisfiedException class.
ConstraintSatisfiedException(String, Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException
Initializes a new instance of the ConstraintSatisfiedException class with a specified error message and a reference to the inner exception that is the cause of this exception.
ConstraintSatisfiedException(Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException
Initializes a new instance of the ConstraintSatisfiedException class a reference to the inner exception that is the cause of this exception.
contains(Object) - Method in class com.bayesserver.data.sampling.ExcludedVariables
Determines whether the specified variable is excluded.
contains(T) - Method in class com.bayesserver.Interval
Determines whether a value is within this interval.
contains(String) - Method in interface com.bayesserver.NameValuesReader
Determines whether a value exists for a particular name.
contains(Object) - Method in class com.bayesserver.NetworkLinkCollection
Determines whether a Link is in the collection.
contains(Object) - Method in class com.bayesserver.NetworkNodeCollection
Determines whether a Node is in the collection.
contains(Object) - Method in class com.bayesserver.NetworkVariableCollection
Determines whether a Variable is in the collection.
contains(Object) - Method in class com.bayesserver.NodeGroupCollection
Determines whether a group name is in the collection.
contains(Object) - Method in class com.bayesserver.NodeVariableCollection
Determines whether a Variable is in the collection.
contains(Object) - Method in class com.bayesserver.StateCollection
Determines whether a State is in the collection.
contains(Variable) - Method in class com.bayesserver.VariableContextCollection
Determines whether a Variable is in the collection.
contains(VariableContext, boolean) - Method in class com.bayesserver.VariableContextCollection
Determines whether a variable-time (and optionally Head/Tail) combination is contained in the collection.
contains(Variable, Integer) - Method in class com.bayesserver.VariableContextCollection
Determines whether a Variable is in the collection at the specified [time].
containsAll(List<Variable>) - Method in class com.bayesserver.VariableContextCollection
Determines whether all [items] are matched in the collection.
containsAll(List<Variable>, List<Integer>) - Method in class com.bayesserver.VariableContextCollection
Determines whether all [items] are matched in the collection.
containsAll(List<VariableContext>, boolean) - Method in class com.bayesserver.VariableContextCollection
Determines whether all [items] are matched in the collection at the specified times.
containsAll(VariableContextCollection, boolean) - Method in class com.bayesserver.VariableContextCollection
Determines whether all [items] are matched in the collection.
containsAny(VariableContextCollection, boolean) - Method in class com.bayesserver.VariableContextCollection
Determines whether any [items] are matched in the collection.
containsAny(List<Variable>, List<Integer>) - Method in class com.bayesserver.VariableContextCollection
Determines whether any [items] are matched in the collection.
ConvergenceException - Exception in com.bayesserver.inference
Exception raised when an iterative inference algorithm fails to converge to within a given tolerance.
ConvergenceException() - Constructor for exception com.bayesserver.inference.ConvergenceException
Initializes a new instance of the ConvergenceException class.
ConvergenceException(String) - Constructor for exception com.bayesserver.inference.ConvergenceException
Initializes a new instance of the ConvergenceException class.
ConvergenceException(String, Exception) - Constructor for exception com.bayesserver.inference.ConvergenceException
Initializes a new instance of the ConvergenceException class.
ConvergenceMethod - Enum in com.bayesserver.learning.parameters
The method used to determine whether learning has converged.
copy() - Method in class com.bayesserver.CLGaussian
Creates a copy of the distribution.
copy(Integer) - Method in class com.bayesserver.CLGaussian
Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
copy() - Method in class com.bayesserver.CustomProperty
Makes a copy of this instance.
copy() - Method in class com.bayesserver.data.DataColumn
Copies the DataColumn instance.
copy() - Method in class com.bayesserver.data.DataRow
Creates a copy of this instance.
copy() - Method in class com.bayesserver.data.DataTable
Copies both the structure and data in the DataTable.
copy(boolean) - Method in class com.bayesserver.data.DataTable
Copies the structure and optionally the data in the DataTable.
copy(Variable) - Method in class com.bayesserver.data.VariableReference
Creates a copy of this instance, but based on a different variable.
copy() - Method in interface com.bayesserver.Distribution
Creates a copy of the distribution.
copy(Integer) - Method in interface com.bayesserver.Distribution
Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
copy(Evidence) - Method in class com.bayesserver.inference.DefaultEvidence
Replaces the current evidence, with that from another Evidence instance.
copy(Evidence, Variable) - Method in class com.bayesserver.inference.DefaultEvidence
Replaces the current evidence for an individual variable, with that from another Evidence instance.
copy(Evidence, Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Replaces the current evidence for an individual variable at a specific time, with that from another Evidence instance.
copy(Evidence) - Method in interface com.bayesserver.inference.Evidence
Replaces the current evidence, with that from another Evidence instance.
copy(Evidence, Variable) - Method in interface com.bayesserver.inference.Evidence
Replaces the current evidence for an individual variable, with that from another Evidence instance.
copy(Evidence, Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
Replaces the current evidence for an individual variable at a specific time, with that from another Evidence instance.
copy(Node, Node, int) - Method in class com.bayesserver.Link
Creates a new link, copying the properties from this instance, such as Link.getDescription() and Link.getCustomProperties().
copy() - Method in class com.bayesserver.Network
Makes a copy of the network.
copy() - Method in class com.bayesserver.Node
Makes a copy of this instance.
copy() - Method in class com.bayesserver.NodeDistributionOptions
Copies this instance.
copy() - Method in class com.bayesserver.NodeGroup
Makes a copy of this instance.
copy() - Method in class com.bayesserver.State
Copies this instance.
copy() - Method in class com.bayesserver.Table
Creates a copy of the distribution.
copy(Integer) - Method in class com.bayesserver.Table
Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
copy() - Method in class com.bayesserver.Variable
Copies this instance.
copyFrom(CLGaussian) - Method in class com.bayesserver.CLGaussian
Copies the values from the [source] distribution to this instance.
copyFrom(double[]) - Method in class com.bayesserver.Table
Copies values from the array into the table.
copyFrom(double[]) - Method in class com.bayesserver.TableAccessor
Copies values from an array into the underlying Table using the variable ordering of the TableAccessor, not the Table.getSortedVariables().
copyFrom(double[]) - Method in class com.bayesserver.TableIterator
Resets the iterator and then copies values from an array into the underlying Table using the variable ordering of the TableIterator, not the Table.getSortedVariables().
copyTo(Table) - Method in class com.bayesserver.Table
Copies all values from this instance to the destination Table.
copyTo(double[]) - Method in class com.bayesserver.Table
Copies the table values to an array.
Correlation - Class in com.bayesserver.analysis
Methods to convert covariance matrices to correlation matrices.
count(EvidenceReaderCommand, Evidence) - Static method in class com.bayesserver.data.DataCount
Counts the number of cases that match the current evidence configuration.
create(DataReaderCommand, String, String, String) - Static method in class com.bayesserver.analysis.ConfusionMatrix
Initializes a new instance of the ConfusionMatrix class.
create(String, String, String, Comparable, DataReaderCommand) - Static method in class com.bayesserver.analysis.LiftChart
Creates a lift chart, used to measure predictive performance.
create(DataReaderCommand, String, String) - Static method in class com.bayesserver.analysis.RegressionStatistics
Initializes a new instance of the RegressionStatistics class.
create(DataReaderCommand, String, String, String) - Static method in class com.bayesserver.analysis.RegressionStatistics
Initializes a new instance of the RegressionStatistics class.
create(Network) - Method in class com.bayesserver.data.DataTableEvidenceReaderCommandFactory
Create an evidence reader command, based on a specific network which may be a copy of the original.
create(Network) - Method in interface com.bayesserver.data.EvidenceReaderCommandFactory
Create an evidence reader command, based on a specific network which may be a copy of the original.
createDataReader() - Method in class com.bayesserver.data.DataTable
Create a DataReader based on the DataTable.
createDataReader(T) - Method in interface com.bayesserver.data.distributed.DataPartition
Create a data reader for this distributed partition.
createEvidenceReader(T) - Method in interface com.bayesserver.data.distributed.EvidencePartition
Create an evidence reader for this distributed mapper.
createEvidenceReaderCommand(Network, DataPartitioning) - Method in interface com.bayesserver.analysis.ClusterCountActions
A user supplied function to create an evidence reader command based on a copy of the original network.
createInferenceEngine(Network) - Method in interface com.bayesserver.inference.InferenceFactory
Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.LikelihoodSamplingInferenceFactory
Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.LoopyBeliefInferenceFactory
Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.RelevanceTreeInferenceFactory
Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.VariableEliminationInferenceFactory
Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
createPartitioned(Network, DataPartitioning, int) - Method in class com.bayesserver.data.DataTableEvidenceReaderCommandFactory
Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
createPartitioned(Network, DataPartitioning, int) - Method in interface com.bayesserver.data.EvidenceReaderCommandFactory
Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
createQueryOptions() - Method in interface com.bayesserver.inference.InferenceFactory
Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.inference.LikelihoodSamplingInferenceFactory
Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.inference.LoopyBeliefInferenceFactory
Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.inference.RelevanceTreeInferenceFactory
createQueryOptions() - Method in class com.bayesserver.inference.VariableEliminationInferenceFactory
createQueryOutput() - Method in interface com.bayesserver.inference.InferenceFactory
Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.LikelihoodSamplingInferenceFactory
Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.LoopyBeliefInferenceFactory
Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.RelevanceTreeInferenceFactory
Creates a RelevanceTreeQueryOutput instance that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.VariableEliminationInferenceFactory
Creates a VariableEliminationQueryOutput instance that collects information about each query, in addition to the distributions.
CrossValidation - Class in com.bayesserver.data
Allows test metrics/scores to be calculated using cross validation.
CrossValidationActions - Interface in com.bayesserver.data
Actions which the caller must implement to use Cross Validation.
CrossValidationCombineMethod - Enum in com.bayesserver.data
Ways of combining cross validation test results to form an overall cross validation score.
CrossValidationNetwork - Interface in com.bayesserver.data
The result of learning on a single cross validation training partitioning.
CrossValidationOutput - Class in com.bayesserver.data
Details of a Cross-Validation partition.
CrossValidationScore - Interface in com.bayesserver.data
Interface for cross validation scores.
CrossValidationTestResult - Interface in com.bayesserver.data
Interface for cross validation test results.
CustomProperty - Class in com.bayesserver
Stores a custom property.
CustomProperty(String) - Constructor for class com.bayesserver.CustomProperty
Initializes a new instance of the CustomProperty class.
CustomProperty(String, String) - Constructor for class com.bayesserver.CustomProperty
Initializes a new instance of the CustomProperty class.
CustomPropertyCollection - Class in com.bayesserver
Stores custom properties for a variety of objects.

D

Dag - Class in com.bayesserver
Includes methods for testing whether a network is a Directed Acyclic Graph (DAG).
DatabaseDataReaderCommand - Class in com.bayesserver.data
Provides a default implementation of DataReaderCommand for reading databases.
DatabaseDataReaderCommand(String, String) - Constructor for class com.bayesserver.data.DatabaseDataReaderCommand
Initializes a new instance of the DatabaseDataReaderCommand class.
DatabaseDataReaderCommand(String, String, String, String) - Constructor for class com.bayesserver.data.DatabaseDataReaderCommand
Initializes a new instance of the DatabaseDataReaderCommand class.
DatabaseDataReaderCommand(String, String, int) - Constructor for class com.bayesserver.data.DatabaseDataReaderCommand
Initializes a new instance of the DatabaseDataReaderCommand class.
DataColumn - Class in com.bayesserver.data
Class that represents an memory column of data.
DataColumn(String, Class) - Constructor for class com.bayesserver.data.DataColumn
Creates a new instance of the DataColumn class.
DataColumnCollection - Class in com.bayesserver.data
Represents a collection of columns in a DataTable, a simple in-memory data store.
DataCount - Class in com.bayesserver.data
Contains methods to count the number of cases that match a given evidence configuration.
DataCountOutput - Class in com.bayesserver.data
Contains information returned by DataCount.
DataCountOutput() - Constructor for class com.bayesserver.data.DataCountOutput
 
DataIOException - Exception in com.bayesserver.data
Raised when an error occurs reading data from or writing data to a database, a file or other source.
DataIOException() - Constructor for exception com.bayesserver.data.DataIOException
Initializes a new instance of the DataIOException class.
DataIOException(String) - Constructor for exception com.bayesserver.data.DataIOException
Initializes a new instance of the DataIOException class with a specified error message.
DataIOException(String, Throwable) - Constructor for exception com.bayesserver.data.DataIOException
Initializes a new instance of the DataIOException class with a specified error message and a reference to the inner exception that is the cause of this exception.
DataIOException(Throwable) - Constructor for exception com.bayesserver.data.DataIOException
Initializes a new instance of the DataIOException class with a reference to the inner exception that is the cause of this exception.
DataPartition<T> - Interface in com.bayesserver.data.distributed
Interface used by distributed processes that read data.
DataPartitioning - Class in com.bayesserver.data
Determines how data is partitioned.
DataPartitioning(int, DataPartitionMethod, int) - Constructor for class com.bayesserver.data.DataPartitioning
Initializes a new instance of the DataPartitioning class.
DataPartitionMethod - Enum in com.bayesserver.data
Determines whether data is included or excluded from a DataPartitioning.
DataProgress - Interface in com.bayesserver.data
Reports progress on the number of cases read.
DataProgressEventArgs - Class in com.bayesserver.data
Used to provide progress on how many cases have been read.
DataProgressEventArgs() - Constructor for class com.bayesserver.data.DataProgressEventArgs
 
DataReader - Interface in com.bayesserver.data
Interface for reading data row by row.
DataReaderCommand - Interface in com.bayesserver.data
Interface used by EvidenceReader in order to read data multiple times.
DataReaderCommandFiltered - Class in com.bayesserver.data
Wraps an existing data reader command while filtering records.
DataReaderCommandFiltered(DataReaderCommand, DataReaderFilter) - Constructor for class com.bayesserver.data.DataReaderCommandFiltered
Initializes a new instance of the DataReaderCommandFiltered class.
DataReaderFilter - Interface in com.bayesserver.data
Interface to determine whether records should be filtered in a data reader.
DataReaderFiltered - Class in com.bayesserver.data
Wraps an existing data reader while filtering records.
DataReaderFiltered(DataReader, DataReaderFilter) - Constructor for class com.bayesserver.data.DataReaderFiltered
Initializes a new instance of the DataReaderFiltered class.
DataRecord - Interface in com.bayesserver.data
Interface for reading the values from a row of data.
DataRow - Class in com.bayesserver.data
Represents a row of data in a DataTable, a simple in-memory data store.
DataRow(Object[]) - Constructor for class com.bayesserver.data.DataRow
Creates a new instance of a DataRow with the given items.
DataRowCollection - Class in com.bayesserver.data
A collection of rows in a DataTable, a simple in-memory data store.
DataSampler - Class in com.bayesserver.data.sampling
Generates samples from a Bayesian network or Dynamic Bayesian network.
DataSampler(Network) - Constructor for class com.bayesserver.data.sampling.DataSampler
Initializes a new instance of the DataSampler class.
DataSampler(Network, Evidence) - Constructor for class com.bayesserver.data.sampling.DataSampler
Initializes a new instance of the DataSampler class.
DataSamplingOptions - Class in com.bayesserver.data.sampling
Options for data sampling.
DataSamplingOptions() - Constructor for class com.bayesserver.data.sampling.DataSamplingOptions
 
DataTable - Class in com.bayesserver.data
A simple in memory data structure which can be used as an alternative to a data store (such as a database).
DataTable() - Constructor for class com.bayesserver.data.DataTable
Creates a new instance of the DataTable class.
DataTableDataReaderCommand - Class in com.bayesserver.data
A DataReaderCommand backed by a DataTable.
DataTableDataReaderCommand(DataTable) - Constructor for class com.bayesserver.data.DataTableDataReaderCommand
Creates a new instance, based on a DataTable.
DataTableEvidenceReaderCommandFactory - Class in com.bayesserver.data
A default implementation of BayesServer.Data.IEvidenceReaderCommandFactory based on a DataTable and a simple partitioning scheme based on a partition column.
DataTableEvidenceReaderCommandFactory(DataTable, List<VariableReference>, ReaderOptions, String) - Constructor for class com.bayesserver.data.DataTableEvidenceReaderCommandFactory
Initializes a new instance of the DataTableEvidenceReaderCommandFactory class.
DataTableEvidenceReaderCommandFactory(DataTable, List<VariableReference>, TemporalReaderOptions, String) - Constructor for class com.bayesserver.data.DataTableEvidenceReaderCommandFactory
Initializes a new instance of the DataTableEvidenceReaderCommandFactory class.
DataTableEvidenceReaderCommandFactory(DataTable, List<VariableReference>, ReaderOptions, String, DataTable, List<VariableReference>, TemporalReaderOptions, String) - Constructor for class com.bayesserver.data.DataTableEvidenceReaderCommandFactory
Initializes a new instance of the DataTableEvidenceReaderCommandFactory class.
DataTableReader - Class in com.bayesserver.data
Allows a DataTable to be read as a DataReader.
DataTableReader(DataTable) - Constructor for class com.bayesserver.data.DataTableReader
Creats a new DataTableReader instance, backed by a DataTable, a simple in-memory data store.
DecisionAlgorithm - Enum in com.bayesserver.inference
The type of algorithm to use when a network has decision nodes.
DecisionPostProcessingMethod - Enum in com.bayesserver.learning.parameters
The type of post processing to be applied to the distributions of decision nodes at the end of parameter learning.
decompose(Network, DecomposeOptions) - Static method in class com.bayesserver.Decomposer
Decomposes a Bayesian network containing nodes with multiple variables into its single variable node equivalent.
DecomposeOptions - Class in com.bayesserver
Options used by the Decomposer class.
DecomposeOptions() - Constructor for class com.bayesserver.DecomposeOptions
 
DecomposeOutput - Class in com.bayesserver
Decomposer - Class in com.bayesserver
Contains methods to decompose nodes with multiple variables into their single variable equivalents.
DefaultCancellation - Class in com.bayesserver
Class for canceling long running operations.
DefaultCancellation() - Constructor for class com.bayesserver.DefaultCancellation
 
DefaultCrossValidationNetwork - Class in com.bayesserver.data
Default basic implementation of ICrossValidationNetwork.
DefaultCrossValidationNetwork(Network) - Constructor for class com.bayesserver.data.DefaultCrossValidationNetwork
Initializes a new instance of the DefaultCrossValidationNetwork class, with a network.
DefaultCrossValidationScore - Class in com.bayesserver.data
A default simple implementation of ICrossValidationScore.
DefaultCrossValidationScore(double) - Constructor for class com.bayesserver.data.DefaultCrossValidationScore
Initializes a new instance of the DefaultCrossValidationScore class.
DefaultCrossValidationTestResult - Class in com.bayesserver.data
A simple default implementation of CrossValidationTestResult.
DefaultCrossValidationTestResult(double, Object, Double) - Constructor for class com.bayesserver.data.DefaultCrossValidationTestResult
Initializes a new instance of the DefaultCrossValidationTestResult class.
DefaultDataReader - Class in com.bayesserver.data
Reads and validates non temporal and/or temporal data.
DefaultDataReader(DataReader, ReaderOptions) - Constructor for class com.bayesserver.data.DefaultDataReader
Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultDataReader
Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, ReaderOptions, DataReader, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultDataReader
Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, ReaderOptions, List<NestedDataReader>) - Constructor for class com.bayesserver.data.DefaultDataReader
Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, ReaderOptions, DataReader, TemporalReaderOptions, List<NestedDataReader>) - Constructor for class com.bayesserver.data.DefaultDataReader
Initializes a new instance of the DefaultDataReader class.
DefaultEvidence - Class in com.bayesserver.inference
Represents the evidence, or case data (e.g.
DefaultEvidence(Network) - Constructor for class com.bayesserver.inference.DefaultEvidence
Initializes a new instance of the DefaultEvidence class, with the target Bayesian network.
DefaultEvidence(Evidence) - Constructor for class com.bayesserver.inference.DefaultEvidence
Initializes a new instance of the DefaultEvidence class, and copies the evidence from another instance.
DefaultEvidence(DefaultEvidence) - Constructor for class com.bayesserver.inference.DefaultEvidence
Initializes a new instance of the DefaultEvidence class, copying data from an existing DefaultEvidence object.
DefaultEvidenceReader - Class in com.bayesserver.data
Provides a default implementation of EvidenceReader, used in Bayes Server for tasks such as parameter learning.
DefaultEvidenceReader(DataReader, List<VariableReference>, ReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReader
Initializes a new instance of the DefaultEvidenceReader class.
DefaultEvidenceReader(DataReader, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReader
Initializes a new instance of the DefaultEvidenceReader class.
DefaultEvidenceReader(DataReader, List<VariableReference>, ReaderOptions, DataReader, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReader
Initializes a new instance of the DefaultEvidenceReader class, supporting both temporal and non temporal data.
DefaultEvidenceReaderCommand - Class in com.bayesserver.data
Creates instances of EvidenceReader on demand.
DefaultEvidenceReaderCommand(DataReaderCommand, List<VariableReference>, ReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReaderCommand
Initializes a new instance of the DefaultEvidenceReaderCommand class.
DefaultEvidenceReaderCommand(DataReaderCommand, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReaderCommand
Initializes a new instance of the DefaultEvidenceReaderCommand class.
DefaultEvidenceReaderCommand(DataReaderCommand, List<VariableReference>, ReaderOptions, DataReaderCommand, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReaderCommand
Initializes a new instance of the DefaultEvidenceReaderCommand class, supporting both temporal and non temporal data.
DefaultQueryDistributionCollection - Class in com.bayesserver.inference
DefaultQueryDistributionCollection(Network) - Constructor for class com.bayesserver.inference.DefaultQueryDistributionCollection
Initializes a new instance of the DefaultQueryDistributionCollection class, passing the target Bayesian network as a parameter.
DefaultReadOptions - Class in com.bayesserver.data
Provides a default implementation of ReadOptions.
DefaultReadOptions() - Constructor for class com.bayesserver.data.DefaultReadOptions
Initializes a new instance of the DefaultReadOptions class.
DefaultReadOptions(boolean) - Constructor for class com.bayesserver.data.DefaultReadOptions
Initializes a new instance of the DefaultReadOptions class.
detect(Network, Variable, List<Integer>, ClusterCountActions, ClusterCountOptions) - Static method in class com.bayesserver.analysis.ClusterCount
Determine the number of clusters (discrete states of a latent variable) using cross validation.
detect(List<Variable>, EvidenceReaderCommand, Variable, FeatureSelectionOptions) - Static method in class com.bayesserver.learning.structure.FeatureSelection
Determines which variables are likely to be good features (predictors) of a target variable.
DiscretePriorMethod - Enum in com.bayesserver.learning.parameters
The type of discrete prior to use for discrete distributions during parameter learning.
DiscretizationAlgoOptions - Class in com.bayesserver.data.discovery
Options for a discretization algorithm.
DiscretizationAlgoOptions() - Constructor for class com.bayesserver.data.discovery.DiscretizationAlgoOptions
Initializes a new instance of the com.bayesserver.data.discovery.DiscretizationAlgorithmOptions class.
DiscretizationAlgoOptions(String) - Constructor for class com.bayesserver.data.discovery.DiscretizationAlgoOptions
Initializes a new instance of the com.bayesserver.data.discovery.DiscretizationAlgorithmOptions class.
DiscretizationColumn - Class in com.bayesserver.data.discovery
Identifies a column of data and how it is to be discretized.
DiscretizationColumn(String) - Constructor for class com.bayesserver.data.discovery.DiscretizationColumn
Initializes a new instance of the DiscretizationColumn class.
DiscretizationInfo - Class in com.bayesserver.data.discovery
Discretization information for column of data, returned from a discretization algorithm.
DiscretizationMethod - Enum in com.bayesserver.data.discovery
The method (algorithm) to use for discretization of continuous data.
DiscretizationOptions - Class in com.bayesserver.data.discovery
Options that determine whether and how continuous data should be discretized.
DiscretizationOptions() - Constructor for class com.bayesserver.data.discovery.DiscretizationOptions
 
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.Clustering
Discretizes unsorted continuous data that may contain missing (null) values.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in class com.bayesserver.data.discovery.Clustering
Discretizes one or more data columns, that may contain missing (null) values.
Discretize - Interface in com.bayesserver.data.discovery
Interface which a discretization algorithm must implement.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in interface com.bayesserver.data.discovery.Discretize
Discretizes one or more data columns, that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in interface com.bayesserver.data.discovery.Discretize
Discretizes unsorted continuous data that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualFrequencies
Discretizes unsorted continuous data that may contain missing (null) values.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in class com.bayesserver.data.discovery.EqualFrequencies
Discretizes one or more data columns, that may contain missing (null) values.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in class com.bayesserver.data.discovery.EqualIntervals
Discretizes one or more data columns, that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualIntervals
Discretizes unsorted continuous data that may contain missing (null) values.
DiscretizeProgress - Interface in com.bayesserver.data.discovery
Interface to provide progress information during discretization.
DiscretizeProgressInfo - Interface in com.bayesserver.data.discovery
Interface to provide progress information during discretization.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.Clustering
Discretizes unsorted weighted continuous data that may contain missing (null) values.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in interface com.bayesserver.data.discovery.Discretize
Discretizes unsorted weighted continuous data that may contain missing (null) values.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualFrequencies
Discretizes unsorted weighted continuous data that may contain missing (null) values.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualIntervals
Discretizes unsorted weighted continuous data that may contain missing (null) values.
distribute(T) - Method in interface com.bayesserver.Distributer
The implementor should distribute the processing.
DistributedMapperContext - Class in com.bayesserver.learning.parameters
Contains information used during distributed parameter learning.
DistributedMapperContext() - Constructor for class com.bayesserver.learning.parameters.DistributedMapperContext
 
Distributer<T> - Interface in com.bayesserver
 
DistributerContext - Class in com.bayesserver.learning.parameters
Contains contextual information about the process/iteration being distributed.
Distribution - Interface in com.bayesserver
Interface specifying the required methods and properties for a probability distribution.
distributionChanged(Node, NodeDistributionKey, NodeDistributionKind, Distribution, Distribution) - Method in interface com.bayesserver.NetworkMonitor
For internal use.
DistributionMonitoring - Enum in com.bayesserver.learning.parameters
Indicates which distribution to monitor during learning.
DistributionSpecification - Class in com.bayesserver.learning.parameters
Identifies a node's distribution to learn, and options for learning.
DistributionSpecification(Node) - Constructor for class com.bayesserver.learning.parameters.DistributionSpecification
Initializes a new instance of the DistributionSpecification class.
DistributionSpecification(Node, int) - Constructor for class com.bayesserver.learning.parameters.DistributionSpecification
Initializes a new instance of the DistributionSpecification class.
DistributionSpecification(Node, NodeDistributionKey) - Constructor for class com.bayesserver.learning.parameters.DistributionSpecification
Initializes a new instance of the DistributionSpecification class.
divergence(Distribution, Distribution, LogarithmBase) - Static method in class com.bayesserver.statistics.JensenShannon
Calculates the Jensen Shannon divergence between two distributions.
divergence(Distribution, Distribution, LogarithmBase) - Static method in class com.bayesserver.statistics.KullbackLeibler
Calculates the Kullback-Leibler divergence D(P||Q).
divide(Distribution) - Method in class com.bayesserver.CLGaussian
Creates a new distribution by dividing this instance by the [subset].
divide(CLGaussian) - Method in class com.bayesserver.CLGaussian
Creates a new distribution by dividing this instance by the [subset].
divide(Distribution) - Method in interface com.bayesserver.Distribution
Creates a new distribution by dividing the instance by the specified subset.
divide(Distribution) - Method in class com.bayesserver.Table
Creates a new distribution by dividing this instance by the [subset].
divideByPrior(Table, Table) - Static method in class com.bayesserver.inference.SoftEvidence
Divides target soft evidence by an existing prior distribution or query.
divideInPlace(Table) - Method in class com.bayesserver.Table
Divides this instance in place by the [subset].
DSeparation - Class in com.bayesserver.analysis
Contains methods to calculate D-Separation.
DSeparationCategory - Enum in com.bayesserver.analysis
The result of a D-Separation test.
DSeparationOptions - Class in com.bayesserver.analysis
Options for calculating D-Separation.
DSeparationOptions() - Constructor for class com.bayesserver.analysis.DSeparationOptions
 
DSeparationOutput - Class in com.bayesserver.analysis
Contains the results of a test for D-Separation.
DSeparationTestResult - Class in com.bayesserver.analysis
The result of a D-Separation check for a test node.
DSeparationTestResultCollection - Class in com.bayesserver.analysis
Collection of D-Separation test results.

E

EmpiricalDensity - Interface in com.bayesserver.analysis
Represents an empirical density function, which can represent arbitrary univariate distributions.
EmptyStringAction - Enum in com.bayesserver.data
Determines the action to take when an empty string is encountered.
endUpdate() - Method in class com.bayesserver.inference.DefaultEvidence
Enables change notifications (if available).
endUpdate() - Method in interface com.bayesserver.inference.Evidence
Enables change notifications (if available).
Entropy - Class in com.bayesserver.statistics
Calculates entropy, joint entropy or conditional entropy, which can be used to determine the uncertainty in the states of a discrete distribution.
entrySet() - Method in class com.bayesserver.NodeDistributions
 
enumerate(List<Variable>, CombinationAction, CombinationOptions) - Static method in class com.bayesserver.analysis.Combinations
Enumerates the state combinations for a set of variables.
enumerate(int[], CombinationAction, CombinationOptions) - Static method in class com.bayesserver.analysis.Combinations
Enumerates the combinations for a set of counts.
EqualFrequencies - Class in com.bayesserver.data.discovery
Discretizes continuous data in bins, such that each bin contain a similar number of data points.
EqualFrequencies() - Constructor for class com.bayesserver.data.discovery.EqualFrequencies
 
EqualIntervals - Class in com.bayesserver.data.discovery
Discretizes continuous data in bins, such that the bins have equal size.
EqualIntervals() - Constructor for class com.bayesserver.data.discovery.EqualIntervals
 
equals(Object) - Method in class com.bayesserver.Bounds
 
equals(Object) - Method in class com.bayesserver.data.discovery.WeightedValue
 
equals(Object) - Method in class com.bayesserver.Interval
 
equals(Object) - Method in class com.bayesserver.NodeDistributionKey
equals(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributionKey
Indicates whether the current object is equal to another object of the same type.
equals(Object) - Method in class com.bayesserver.StateContext
 
equals(Object) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
 
equals(Object) - Method in class com.bayesserver.ValidationOptions
 
evaluate(double) - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Evaluates the Sensitivity function P(h|e)(t) = (alpha * t + beta) / (gamma * t + delta)
evaluate(double, double) - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Evaluates the Sensitivity function P(h|e)(t1,t2) = (alpha1 * t1 * t2 + beta1 * t1 + gamma1 * t2 + delta1) / (alpha2 * t1 * t2 + beta2 * t1 + gamma2 * t2 + delta2)
evaluateDeriv(double) - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Evaluates the partial derivative of the Sensitivity function with respect to t = ((alpha*delta)-(beta*gamma))/((gamma*t + delta)^2)
Evidence - Interface in com.bayesserver.inference
Represents the evidence, or case data (e.g.
EvidencePartition<T> - Interface in com.bayesserver.data.distributed
Interface used by distributed processes that read evidence.
EvidenceReader - Interface in com.bayesserver.data
A data set iterator, that can be read multiple times.
EvidenceReaderCommand - Interface in com.bayesserver.data
Interface used to create instances of EvidenceReader.
EvidenceReaderCommandFactory - Interface in com.bayesserver.data
Creates evidence reader commands, for repeated iterating of a data set/partition of a data set.
EvidenceReaderEventArgs - Class in com.bayesserver.data
Contains a reference to a reader created by a reader command.
EvidenceType - Enum in com.bayesserver.inference
The type of evidence for a variable.
ExcludedVariables - Class in com.bayesserver.data.sampling
Set of variables which should be excluded from an operation, such as missing data generation.
execute(Integer[]) - Method in interface com.bayesserver.analysis.CombinationAction
execute(ImpactOutputItem) - Method in interface com.bayesserver.analysis.ImpactAction
execute(LogLikelihoodAnalysisOutputItem) - Method in interface com.bayesserver.analysis.LogLikelihoodAnalysisAction
execute(DataProgressEventArgs) - Method in interface com.bayesserver.data.DataProgress
Called by an algorithm to report progress on the number of cases read.
execute(EvidenceReaderEventArgs) - Method in interface com.bayesserver.data.ExecuteEvidenceReader
Called when an evidence reader is created.
execute() - Method in interface com.bayesserver.MultipleIterator.Combination
 
ExecuteEvidenceReader - Interface in com.bayesserver.data
Used to receive notification of a new Evidence reader being created from an evidence reader command.
executeReader() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Returns an instance of IDataReader.
executeReader() - Method in interface com.bayesserver.data.DataReaderCommand
Returns an instance of IDataReader.
executeReader() - Method in class com.bayesserver.data.DataReaderCommandFiltered
Returns an instance of IDataReader.
executeReader() - Method in class com.bayesserver.data.DataTableDataReaderCommand
Creates a new DataReader backed by the DataTable.
executeReader() - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand
Returns an instance of IEvidenceReader which allows evidence to be iterated over.
executeReader() - Method in interface com.bayesserver.data.EvidenceReaderCommand
Returns an instance of IEvidenceReader which allows evidence to be iterated over.
executeReader() - Method in class com.bayesserver.data.timeseries.WindowDataReaderCommand
Returns an instance of IDataReader.

F

FeatureSelection - Class in com.bayesserver.learning.structure
Contains methods to determine which variables are likely to be good features (predictors) or not.
FeatureSelectionOptions - Class in com.bayesserver.learning.structure
Options governing the tests carried out to determine whether variables are likely to be features (predictors) of a target variable.
FeatureSelectionOptions() - Constructor for class com.bayesserver.learning.structure.FeatureSelectionOptions
 
FeatureSelectionOutput - Class in com.bayesserver.learning.structure
FeatureSelectionTest - Class in com.bayesserver.learning.structure
Contains information about a test carried out between a variable and a target to determine whether the variable is likely to be a feature or not.
finalize() - Method in class com.bayesserver.data.DefaultEvidenceReader
 
find(Node, Node) - Method in class com.bayesserver.NetworkLinkCollection
Finds a link from one node to another if it exists, otherwise returns null.
find(Node, Node, int) - Method in class com.bayesserver.NetworkLinkCollection
Finds a link from one node to another if it exists, otherwise returns null.
findByValue(Object) - Method in class com.bayesserver.StateCollection
Finds the state whose value/> matches the given [value], or null if a match is not found.
findForTime(int, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
Finds the temporal distribution that is suitable for the time specified.
findForTime(int) - Method in class com.bayesserver.NodeDistributions
Finds the temporal distribution that is suitable for the time specified.
findForTimeWithOrder(int) - Method in class com.bayesserver.NodeDistributions
Finds the temporal distribution that is suitable for the time specified.
findForTimeWithOrder(int, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
Finds the temporal distribution that is suitable for the time specified.
findStateByValue(Object) - Method in class com.bayesserver.Variable
Finds a state based on a state value.
fromCovariance(CLGaussian, int) - Static method in class com.bayesserver.analysis.Correlation
Convert a covariance matrix to a correlation matrix.
fromCovariance(CLGaussian, State...) - Static method in class com.bayesserver.analysis.Correlation
 
fromCovariance(CLGaussian, StateContext...) - Static method in class com.bayesserver.analysis.Correlation
 

G

generate(DataReaderCommand, List<VariableDefinition>, VariableGeneratorOptions) - Static method in class com.bayesserver.data.discovery.VariableGenerator
Generates variables from a data source.
get(int) - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
 
get(int) - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
 
get(int) - Method in class com.bayesserver.analysis.DSeparationTestResultCollection
 
get(String) - Method in class com.bayesserver.CustomPropertyCollection
Gets the CustomProperty with the specified name, from the collection, or returns null if not found.
get(int) - Method in class com.bayesserver.CustomPropertyCollection
 
get(String) - Method in class com.bayesserver.data.DataColumnCollection
Gets the column with the specified name, or null if the name if not found.
get(int) - Method in class com.bayesserver.data.DataColumnCollection
Gets the DataColumn at the given index.
get(int) - Method in class com.bayesserver.data.DataRow
Gets the value at the specified index.
get(int) - Method in class com.bayesserver.data.DataRowCollection
Gets the row at the given index.
get(int) - Method in class com.bayesserver.data.sampling.ExcludedVariables
 
get(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the hard evidence for a discrete variable or continuous variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the evidence for a discrete variable at the specified time.
get(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the evidence for a node with a single variable at the specified time.
get(Variable, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the evidence for a temporal variable.
get(Node, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the evidence for a node's single temporal variable.
get(Node) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the hard evidence value for a particular node's variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(int) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
 
get(Variable) - Method in interface com.bayesserver.inference.Evidence
Gets the hard evidence for a discrete variable or continuous variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
Gets the evidence for a discrete variable at the specified time.
get(Variable, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
Gets the evidence for a temporal variable.
get(Node, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
Gets the evidence for a node's single temporal variable.
get(Node) - Method in interface com.bayesserver.inference.Evidence
Gets the hard evidence value for a particular node's variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
Gets the evidence for a node with a single variable at the specified time.
get(int) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
 
get(int) - Method in class com.bayesserver.NetworkLinkCollection
Gets the Link object at the specified index.
get(int) - Method in class com.bayesserver.NetworkNodeCollection
Gets the Node object at the specified index.
get(String) - Method in class com.bayesserver.NetworkNodeCollection
Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.NetworkNodeCollection
Performs a case sensitive lookup.
get(String) - Method in class com.bayesserver.NetworkNodeGroupCollection
Gets the NodeGroup with the specified name, from the collection, or returns null if not found.
get(int) - Method in class com.bayesserver.NetworkNodeGroupCollection
 
get(String) - Method in class com.bayesserver.NetworkVariableCollection
Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.NetworkVariableCollection
Performs a case sensitive lookup.
get(int) - Method in class com.bayesserver.NetworkVariableCollection
Gets the Variable object at the specified index.
get(int) - Method in class com.bayesserver.NodeDistributions
Gets a distribution at a particular temporal order.
get(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions
Gets a distribution with particular properties, such as temporal order.
get(NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
Gets a particular kind of distribution on the node.
get(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
Gets a distribution with particular properties, such as temporal order.
get(int) - Method in class com.bayesserver.NodeGroupCollection
Gets the group at the specified index.
get(int) - Method in class com.bayesserver.NodeLinkCollection
 
get(int) - Method in class com.bayesserver.NodeVariableCollection
Gets the Variable object at the specified index.
get(String) - Method in class com.bayesserver.NodeVariableCollection
Performs a case sensitive lookup.
get(String) - Method in class com.bayesserver.StateCollection
Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.StateCollection
Performs a case sensitive lookup.
get(int) - Method in class com.bayesserver.StateCollection
Gets the State at the specified index.
get(State...) - Method in class com.bayesserver.Table
Gets the table value corresponding to the given states.
get(StateContext...) - Method in class com.bayesserver.Table
Gets the table value corresponding to the given states and associated times.
get(int) - Method in class com.bayesserver.Table
Gets the Table value at the specified index into the 1-dimensional array.
get(int[]) - Method in class com.bayesserver.TableAccessor
Gets the underlying Table value, using states corresponding to the order of variables in the TableAccessor.
get(int) - Method in class com.bayesserver.TableAccessor
Gets the underlying Table value, specified i.
get(int) - Method in class com.bayesserver.VariableContextCollection
Gets the Variable object at the specified index.
get(int) - Method in class com.bayesserver.VariableMap
Maps between the custom order and the sorted collection.
getA() - Method in class com.bayesserver.learning.structure.LinkConstraint
Gets the first node involved in the constraint.
getAccuracy() - Method in class com.bayesserver.analysis.ConfusionMatrix
Gets the overall accuracy of the predictions, which is simply the ConfusionMatrix.getCorrectCount() divided by the ConfusionMatrix.getTotalCount().
getActual() - Method in class com.bayesserver.analysis.LiftChart
Gets the name of the data column containing the actual classification.
getAddNodeGroups() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets a value which determines whether network node groups are added for each group in a level.
getAllowNullDistributions() - Method in class com.bayesserver.ValidationOptions
Determines whether validation should succeed even if the required distribution(s) have not been assigned to a node.
getAlpha() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets Alpha from the sensitivity function.
getAlpha1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Alpha1 from the sensitivity function.
getAlpha2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Alpha2 from the sensitivity function.
getAnomalyScore() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput
Gets a value between [0, 1] with values closer to 0 being more likely to be anomalous.
getAutoCommit() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Gets the auto commit value to be set on each connection created.
getAutoDetectDiscreteLimit() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets the distinct value count, which when exceeded changes a variable from discrete to continuous.
getAutoReadTemporal() - Method in class com.bayesserver.data.DefaultEvidenceReader
Determines whether any temporal data is read automatically.
getB() - Method in class com.bayesserver.learning.structure.LinkConstraint
Gets the second node involved in the constraint.
getBaseEvidence() - Method in interface com.bayesserver.inference.Inference
Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.LoopyBeliefInference
Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.RelevanceTreeInference
Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.VariableEliminationInference
Optional evidence which can be used to calculate the lift of queries.
getBaseline() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutput
Gets baseline log-likelihood values.
getBestToWorst() - Method in class com.bayesserver.analysis.ClusterCountOutput
A list of scores, sorted from best to worst.
getBeta() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets Beta from the sensitivity function.
getBeta1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Beta1 from the sensitivity function.
getBeta2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Beta2 from the sensitivity function.
getBIC() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the Bayesian Information Criterion (BIC) for the final learnt Network based on the learning data.
getBoolean(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getBoolean(int) - Method in interface com.bayesserver.data.DataRecord
Gets a boolean value for the specified column.
getBoolean(int) - Method in class com.bayesserver.data.DataTableReader
 
getBoolean(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets a boolean value for the specified column.
getBounds() - Method in class com.bayesserver.Node
Gets the size and location of the node.
getCalculateStatistics() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets a value indicating whether to calculate summary statistics in an extra iteration at the end of learning.
getCancel() - Method in interface com.bayesserver.Cancellation
When set to true attempts to cancel a long running operation.
getCancel() - Method in class com.bayesserver.DefaultCancellation
When set to true attempts to cancel a long running operation.
getCancellation() - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
Gets of sets an instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets of sets an instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Allows cancellation of a query.
getCancellation() - Method in interface com.bayesserver.inference.QueryOptions
Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.TreeQueryOptions
Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
Used to cancel a long running operation.
getCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the number of cases (records) in the learning data.
getCaseId() - Method in class com.bayesserver.data.ReadInfo
The current case id.
getCaseIdColumn() - Method in class com.bayesserver.data.NestedDataReader
The name of the case identifier column, which links to the case table.
getCaseIdColumn() - Method in class com.bayesserver.data.ReaderOptions
The name of the case identifier column, if one is present.
getCaseIdColumn() - Method in class com.bayesserver.data.TemporalReaderOptions
The name of the temporal case identifier column, if one is present.
getCategory() - Method in class com.bayesserver.analysis.DSeparationTestResult
The test result.
getCell(Comparable, Comparable) - Method in class com.bayesserver.analysis.ConfusionMatrix
Gets information about a cell in a confusion matrix.
getCleared() - Method in class com.bayesserver.data.DefaultReadOptions
Gets a value indicating whether the Evidence has been cleared prior to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions) being called.
getCleared() - Method in interface com.bayesserver.data.ReadOptions
Gets a value indicating whether the Evidence has been cleared prior to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions) being called.
getClusterCount() - Method in class com.bayesserver.analysis.ClusterScore
The number of clusters used to generate this score.
getColumn() - Method in class com.bayesserver.data.VariableReference
Gets the name of the relevant column in the data source.
getColumnCount() - Method in class com.bayesserver.data.DataReaderFiltered
 
getColumnCount() - Method in interface com.bayesserver.data.DataRecord
Gets the number of columns (fields) in the data.
getColumnCount() - Method in class com.bayesserver.data.DataTableReader
 
getColumnCount() - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets the number of columns (fields) in the data.
getColumnIndex(String) - Method in class com.bayesserver.data.DataReaderFiltered
 
getColumnIndex(String) - Method in interface com.bayesserver.data.DataRecord
Gets the zero based column index for a column name.
getColumnIndex(String) - Method in class com.bayesserver.data.DataTableReader
 
getColumnIndex(String) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets the zero based column index for a column name.
getColumnName() - Method in class com.bayesserver.data.DataColumn
 
getColumnName(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getColumnName(int) - Method in interface com.bayesserver.data.DataRecord
Gets the name of the column at the specified index.
getColumnName(int) - Method in class com.bayesserver.data.DataTableReader
 
getColumnName() - Method in class com.bayesserver.data.discovery.DiscretizationColumn
Gets the name of the column of data to be discretized.
getColumnName(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets the name of the column at the specified index.
getColumns() - Method in class com.bayesserver.data.DataTable
Gets the columns in the table.
getColumnType(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getColumnType(int) - Method in interface com.bayesserver.data.DataRecord
Get the data type for the specified column.
getColumnType(int) - Method in class com.bayesserver.data.DataTableReader
 
getColumnType(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Get the data type for the specified column.
getColumnValueType() - Method in class com.bayesserver.data.VariableReference
Gets the type of value in the bound data column.
getComparison() - Method in class com.bayesserver.inference.QueryDistribution
Gets a value indicating whether queried values should be adjusted to show how they compare to the same query with no evidence, or base evidence.
getConfiguration() - Method in interface com.bayesserver.Distributer
Gets configuration name value pairs which must be made available to the distributed workers.
getConflict() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
Gets the conflict measure.
getConflict() - Method in interface com.bayesserver.inference.QueryOptions
Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in interface com.bayesserver.inference.QueryOutput
Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
Gets the conflict measure.
getContinuous() - Method in class com.bayesserver.learning.parameters.Priors
Gets the amount continuous distributions are adjusted during learning.
getContinuousTargetInterval() - Method in class com.bayesserver.analysis.AutoInsightOutput
Gets the target interval (if any).
getConverged() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets a value indicating whether this parameter learning converged.
getConvergenceMethod() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the method used to determine convergence of the learning algorithm.
getCorrectCount() - Method in class com.bayesserver.analysis.ConfusionMatrix
Gets the total number of correct predictions.
getCounts() - Method in class com.bayesserver.data.discovery.VariableInfo
Gets counts such as missing and non-missing data for the variable.
getCovariance(int, int, int) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getCovariance(Variable, Variable, State...) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Variable) - Method in class com.bayesserver.CLGaussian
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
getCovariance(Variable, Integer, Variable, Integer) - Method in class com.bayesserver.CLGaussian
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
getCovariance(Variable, Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Integer, Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Integer, Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(VariableContext, VariableContext, State...) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(VariableContext, VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Integer, Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(VariableContext, VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCustomProperties() - Method in class com.bayesserver.Link
Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.Network
Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.Node
Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.NodeGroup
Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.State
Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.Variable
Gets custom properties associated with this instance.
getDataColumn() - Method in class com.bayesserver.data.discovery.VariableDefinition
The name of the data column, containing the data used to generate the new variable.
getDataProgress() - Method in class com.bayesserver.data.DefaultEvidenceReader
Gets the instance used to report progress on the number of cases read.
getDataProgress() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Reports progress on the number of cases read.
getDataProgressInterval() - Method in class com.bayesserver.data.DefaultEvidenceReader
Gets a value which determines how often progress events are raised.
getDataReader() - Method in class com.bayesserver.data.NestedDataReader
Gets the nested data reader.
getDataType() - Method in class com.bayesserver.data.DataColumn
Gets the type of data the column contains.
getDbn() - Method in class com.bayesserver.UnrollOutput
Gets the Dynamic Bayesian network before it was unrolled.
getDbnNode(Node) - Method in class com.bayesserver.UnrollOutput
Maps from a node in the unrolled network to the corresponding node in the original Dynamic Bayesian network.
getDbnVariable(Variable) - Method in class com.bayesserver.UnrollOutput
Maps from a variable in the unrolled network to the corresponding variable in the original Dynamic Bayesian network.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in interface com.bayesserver.inference.QueryOptions
Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.learning.parameters.OnlineLearningOptions
Gets the algorithm to use for adaption of decision graphs.
getDecisionPostProcessing() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the post processing method for decision nodes.
getDecomposedNetwork() - Method in class com.bayesserver.DecomposeOutput
Gets the network, which is the decomposed equivalent of the original network.
getDecomposedVariable(Variable) - Method in class com.bayesserver.DecomposeOutput
Maps a variable in the original network to the equivalent variable in the decomposed network.
getDefault() - Static method in class com.bayesserver.NodeDistributionKey
Gets a default instance, which is equivalent to constructing a new instance with the default constructor.
getDelta() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets Delta from the sensitivity function.
getDelta() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
Gets the relative change in parameters used to determine convergence.
getDelta1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Delta1 from the sensitivity function.
getDelta2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Delta2 from the sensitivity function.
getDescription() - Method in class com.bayesserver.CustomProperty
An optional description for the custom property.
getDescription() - Method in class com.bayesserver.Link
Optional description for the link.
getDescription() - Method in class com.bayesserver.Network
An optional description for the Bayesian network.
getDescription() - Method in class com.bayesserver.Node
An optional description for the node.
getDescription() - Method in class com.bayesserver.NodeGroup
An optional description for the custom property.
getDescription() - Method in class com.bayesserver.State
Gets an optional description for the state.
getDescription() - Method in class com.bayesserver.Variable
An optional description for the variable.
getDetectIntegralFloats() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets a value, which when true tests floating point column data to see if the data is an integral type, which would then become a candidate to be a discrete variable when VariableValueType is not specified.
getDifference() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the difference between the probability of this state given the target state and the probability of this state excluding the target state.
getDiscrete() - Method in class com.bayesserver.learning.parameters.Priors
Gets the amount distributions containing discrete variables are adjusted during learning.
getDiscretePriorMethod() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Gets the type of discrete prior to use for this distribution.
getDiscretePriorMethod() - Method in class com.bayesserver.learning.parameters.Priors
The default discrete prior to use for discrete distributions during parameter learning.
getDiscretizationMethod() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets the method (algorithm) to use for discretization, if any.
getDiscretizationOptions() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets options that specify how continuous data should be discretized, if DiscretizationMethod is not DiscretizationMethod.NONE.
getDistance() - Method in class com.bayesserver.inference.QueryDistribution
The distance between this query calculated with base evidence or no evidence, and when calculated with evidence.
getDistribution() - Method in class com.bayesserver.inference.QueryDistribution
Gets the distribution to query.
getDistribution() - Method in class com.bayesserver.Node
Returns the distribution currently associated with the Node.
getDistribution() - Method in class com.bayesserver.NodeDistributions.DistributionOrder
Gets the distribution.
getDistributionMonitoring() - Method in interface com.bayesserver.learning.parameters.ParameterLearningProgress
Gets information about the current state of distributions being monitored.
getDistributionOptions() - Method in class com.bayesserver.Node
Options that apply to all distributions of this instance.
getDistributions() - Method in class com.bayesserver.Node
Returns the distributions associated with this instance with NodeDistributionKind = Probability.
getDouble(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getDouble(int) - Method in interface com.bayesserver.data.DataRecord
Gets a double value for the specified column.
getDouble(int) - Method in class com.bayesserver.data.DataTableReader
 
getDouble(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets a double value for the specified column.
getEmptyStringAction() - Method in class com.bayesserver.data.discovery.VariableDefinition
Determines the action to take if an empty string is encountered.
getEmptyStringAction() - Method in class com.bayesserver.data.VariableReference
Determines the action to take if an empty string is encountered.
getEnsureTestWithoutCluster() - Method in class com.bayesserver.analysis.ClusterCountOptions
Gets a value which indicates whether a test must be included which excludes the cluster variable altogether.
getEntropyX() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets the entropy for X.
getEntropyY() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets the entropy for Y.
getEnumerateAllMissing() - Method in class com.bayesserver.analysis.CombinationOptions
Gets a value which indicates whether the combination where all states are null/missing should be included in the enumeration.
getEnumerateMissing() - Method in class com.bayesserver.analysis.CombinationOptions
Gets a value which indicates whether null/missing values should be enumerated in addition to each state.
getEvidence() - Method in interface com.bayesserver.inference.Inference
Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.LoopyBeliefInference
Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.RelevanceTreeInference
Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.VariableEliminationInference
Gets the evidence (case data, e.g.
getEvidence() - Method in class com.bayesserver.learning.parameters.OnlineLearning
Gets the evidence used internally.
getEvidenceFlags() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets a list of values each of which indicate which of the evidence being analyzed is set.
getEvidenceFlags() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutputItem
Gets a list of values each of which indicate which of the evidence being analyzed is set.
getEvidenceReader() - Method in class com.bayesserver.data.EvidenceReaderEventArgs
Gets the reader created by a reader command.
getEvidenceType(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
Returns the type of evidence currently set for a variable.
getEvidenceType(Node) - Method in class com.bayesserver.inference.DefaultEvidence
Returns the type of evidence currently set for a node with a single variable.
getEvidenceType(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Returns the type of evidence currently set for a node with a single variable at a given time.
getEvidenceType(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Returns the type of evidence currently set for a variable at a given time.
getEvidenceType(Variable) - Method in interface com.bayesserver.inference.Evidence
Returns the type of evidence currently set for a variable.
getEvidenceType(Node) - Method in interface com.bayesserver.inference.Evidence
Returns the type of evidence currently set for a node with a single variable.
getEvidenceType(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
Returns the type of evidence currently set for a node with a single variable at a given time.
getEvidenceType(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
Returns the type of evidence currently set for a variable at a given time.
getExcludeNullDistributions() - Method in class com.bayesserver.ParameterCountOptions
Gets a value indicating whether null distributions are excluded from the parameter count.
getFactory() - Method in class com.bayesserver.analysis.ImpactOptions
Gets the inference factory which is used to create inference engines during an impact analysis.
getFactory() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Gets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
getFailureMode() - Method in class com.bayesserver.learning.structure.LinkConstraint
Gets the action to take when this link constraint is violated.
getFetchSize() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Gets the fetch size to be set on each statement created.
getFloat(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getFloat(int) - Method in interface com.bayesserver.data.DataRecord
Gets a float value for the specified column.
getFloat(int) - Method in class com.bayesserver.data.DataTableReader
 
getFloat(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets a float value for the specified column.
getFrom() - Method in class com.bayesserver.Link
The parent node of the directed link.
getGamma() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets Gamma from the sensitivity function.
getGamma1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Gamma1 from the sensitivity function.
getGamma2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets Gamma2 from the sensitivity function.
getGap() - Method in class com.bayesserver.DecomposeOptions
The gap between decomposed nodes, used when laying out new nodes.
getGroups() - Method in class com.bayesserver.Node
Gets the groups this node belongs to.
getHasTemporalReader() - Method in class com.bayesserver.data.DefaultDataReader
Gets a value indicating whether the reader includes temporal data.
getHasZeroIntercepts() - Method in class com.bayesserver.NodeDistributionOptions
Determines whether CLGaussian intercept terms are fixed to zero.
getHeadTail() - Method in class com.bayesserver.VariableContext
Specifies whether the variable is marked as Head or Tail.
getHeight() - Method in class com.bayesserver.Bounds
Gets the height of the element.
getHypothesis() - Method in class com.bayesserver.analysis.ImpactOutput
Gets output information for the hypothesis variable/state.
getHypothesisImprovement() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
Gets the improvement between the hypothesis statistic when we do not have knowledge about this test variable and when we do.
getHypothesisStatistic() - Method in class com.bayesserver.analysis.ValueOfInformationOutput
Gets the statistic associated with the hypothesis before any test variables have evidence set.
getHypothesisStatistic() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
Gets the statistic for the hypothesis given knowledge on this test variable.
getIdeal() - Method in class com.bayesserver.analysis.LiftChart
Gets the population probability value at which the target reaches 100 %.
getIncludeGlobalCovariance() - Method in class com.bayesserver.learning.parameters.Priors
When Gaussian distributions are adjusted according to the Priors.getContinuous() prior, this property determines whether the global covariance should be included in the adjustment, as well as the global variance.
getIndependence() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets options controlling how the independence tests are carried out.
getIndex() - Method in class com.bayesserver.Link
The Index of this instance in the collection of links belonging to a network, or -1 if the link does not belong to a network.
getIndex() - Method in class com.bayesserver.Node
The Index of this instance in the collection of nodes belonging to a network, or -1 if the node does not belong to a network.
getIndex() - Method in class com.bayesserver.State
Gets the index of the state in a variable's Variable.getStates() collection.
getIndex() - Method in class com.bayesserver.Table.MaxValue
 
getIndex() - Method in class com.bayesserver.Variable
The Index of this instance in the collection of variables belonging to a network, or -1 if the variable does not belong to a node and hence a network.
getInferenceFactory() - Method in class com.bayesserver.analysis.AssociationOptions
Gets the inference factory used for link strength calculations.
getInferenceFactory() - Method in class com.bayesserver.analysis.AutoInsightOptions
Gets the inference factory used for link strength calculations.
getInferenceFactory() - Method in class com.bayesserver.analysis.ClusterCountOptions
Gets the factory which is used to create inference engines during the cluster count tests.
getInferenceFactory() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
Gets the factory which is used to create inference engines during the in-sample anomaly detection process.
getInferenceFactory() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets the inference factory used during scoring.
getInferenceFactory() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets the inference factory used during scoring.
getInferenceFactory() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets the inference factory used during scoring.
getInfiniteExtremes() - Method in class com.bayesserver.data.discovery.DiscretizationOptions
Gets a value indicating whether the first and last intervals extend to negative and positive infinity respectively.
getInitialization() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Options for initialization.
getInitialize() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Gets a flag indicating whether the distribution should be initialized.
getInitializeDistributions() - Method in class com.bayesserver.learning.parameters.InitializationOptions
Indicates whether or not to initialize distributions by default.
getInnerMessage() - Method in class com.bayesserver.data.discovery.VariableGeneratorProgressInfo
Gets an inner progress message.
getInt(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getInt(int) - Method in interface com.bayesserver.data.DataRecord
Gets an integer value for the specified column.
getInt(int) - Method in class com.bayesserver.data.DataTableReader
 
getInt(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets an integer value for the specified column.
getInterval() - Method in class com.bayesserver.analysis.ParameterTuningOneWay
Gets the interval for the parameter which satisfies the constraint used in parameter tuning.
getIntervals() - Method in class com.bayesserver.data.discovery.DiscretizationInfo
Gets the intervals generated by a discretization algorithm for a column of data.
getIsApproximate() - Method in class com.bayesserver.analysis.AutoInsightOutput
Gets a value which when true indicates that the auto-insight calculations were approximated using sampling.
getIsConstant() - Method in class com.bayesserver.data.discovery.VariableInfo
Gets a value which when true indicates that the variable has a constant value.
getIsEnabled() - Method in class com.bayesserver.inference.QueryDistribution
Gets a value indicating whether the distribution should be queried.
getIsImpliedEvidenceEnabled() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets a value indicating whether to detect implied evidence during the calculation.
getIsInternal() - Method in class com.bayesserver.Network
For internal use only.
getIsReadOnly() - Method in class com.bayesserver.StateCollection
Gets a value indicating whether or not the collection is read-only.
getIsValid() - Static method in class com.bayesserver.License
Gets a value indicating whether a license has been successfully validated or not.
getItems() - Method in class com.bayesserver.analysis.ImpactOutput
Gets the output for each combination.
getItems() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutput
Gets the output for each combination.
getIterationCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the number of iterations performed during learning.
getIterationCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
Gets the current iteration count.
getIterations() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
Gets the number of iterations performed.
getJSDivergence() - Method in class com.bayesserver.analysis.AutoInsightOptions
Gets a value which determines the type of Jensen Shannon divergence calculations to perform, if any.
getJSDivergenceBits() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
Gets the Jensen Shannon divergence for the test distribution, measured in BITS.
getKeepEvidenceNotAnalyzed() - Method in class com.bayesserver.analysis.ImpactOptions
Gets a value which when true retains evidence not being analysed, or when false ignores it.
getKeepEvidenceNotAnalyzed() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Gets a value which when true retains evidence not being analysed, or when false ignores it.
getKey() - Method in class com.bayesserver.analysis.ParameterReference
Gets the of the node's distribution being referenced.
getKey() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Gets the order/related node of the distribution.
getKeys() - Method in class com.bayesserver.NodeDistributions
Gets the collection of node distribution keys that require distributions.
getKind() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
Gets the type of Value of information statistic calculated.
getKind() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets the VariableKind for the new variable.
getKind() - Method in class com.bayesserver.Variable
Gets the kind of variable, such as Probability, Decision or Utility.
getKLDivergence() - Method in class com.bayesserver.analysis.AutoInsightOptions
Gets a value which determines the type of KL divergence calculations to perform, if any.
getKLDivergence() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
Gets the Kullback-Leibler divergence for the test distribution, and tells us how much the test variance changes with the hypothesis.
getKLDivergenceFromNone() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the Kullback-Leibler divergence D(P||Q) from the hypothesis query without evidence to analyze set (Q) to the current combination (P).
getKLDivergenceToAll() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the Kullback-Leibler divergence D(P||Q) from the hypothesis query with the current subset of evidence (Q) to all evidence to analyze set (P).
getLift() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the ratio of the probability of this state given the target state over the probability of this state excluding the target state.
getLink() - Method in class com.bayesserver.learning.structure.ChowLiuLinkOutput
Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.ClusteringLinkOutput
Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.HierarchicalLinkOutput
Gets the new link.
getLink() - Method in interface com.bayesserver.learning.structure.LinkOutput
Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.PCLinkOutput
Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.SearchLinkOutput
Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.TANLinkOutput
Gets the new link.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets any link constraints to use during structural learning.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOutput
Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOutput
Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOutput
Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOutput
Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOutput
Gets information about any new links added during the learning process.
getLinkOutputs() - Method in interface com.bayesserver.learning.structure.StructuralLearningOutput
Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOutput
Gets information about any new links added during the learning process.
getLinks() - Method in class com.bayesserver.Network
The collection of links in the Bayesian network.
getLinks() - Method in class com.bayesserver.Node
Collection of both incoming and outgoing links (parent and child nodes).
getLinksIn() - Method in class com.bayesserver.Node
Collection of incoming links (linking to parent nodes).
getLinksOut() - Method in class com.bayesserver.Node
Collection of outgoing links (linking to child nodes).
getLocked() - Method in class com.bayesserver.CLGaussian
Locks or unlocks a distribution.
getLocked() - Method in interface com.bayesserver.Distribution
Locks or unlocks a distribution.
getLocked() - Method in class com.bayesserver.Table
Locks or unlocks a distribution.
getLogarithmBase() - Method in class com.bayesserver.analysis.ValueOfInformationOptions
The logarithm base to use when calculating ValueOfInformation.
getLogLikelihood() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutputItem
Gets the log-likelihood for this output item evidence.
getLogLikelihood() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.QueryDistribution
The log-likelihood specific to the evidence used to calculate this query.
getLogLikelihood() - Method in interface com.bayesserver.inference.QueryOptions
Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in interface com.bayesserver.inference.QueryOutput
Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the log likelihood of the learning data with the final learnt Network.
getLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
Gets the current log likelihood value, if calculated
getLogLikelihoodAll() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisBaselineOutput
Gets the log-likelihood with all evidence to analyze set.
getLogLikelihoodNone() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisBaselineOutput
Gets the log-likelihood with no evidence to analyze set.
getLogWeight() - Method in class com.bayesserver.inference.DefaultEvidence
Gets the natural logarithm of Evidence.getWeight().
getLogWeight() - Method in interface com.bayesserver.inference.Evidence
Gets the natural logarithm of Evidence.getWeight().
getLong(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getLong(int) - Method in interface com.bayesserver.data.DataRecord
Gets a long value for the specified column.
getLong(int) - Method in class com.bayesserver.data.DataTableReader
 
getLong(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets a long value for the specified column.
getMaxEvidenceSubsetSize() - Method in class com.bayesserver.analysis.ImpactOptions
Gets the maximum size of evidence subsets to consider.
getMaxEvidenceSubsetSize() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Gets the maximum size of evidence subsets to consider.
getMaximum() - Method in class com.bayesserver.Interval
Gets the maximum interval value.
getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
getMaximumClusterCount() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets the maximum number of clusters generated.
getMaximumClustersPerGroup() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets the maximum number of clusters generated for each group.
getMaximumConcurrency() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the maximum number of inference engines used during learning.
getMaximumConditional() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets the maximum number of conditional variables to consider during independence testing.
getMaximumEndPoint() - Method in class com.bayesserver.Interval
Gets the end point type for the maximum value of the interval.
getMaximumGroupsPerLevel() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets the maximum number of groups created per level.
getMaximumIterations() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the maximum number of iterations that parameter learning will perform.
getMaximumIterations() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets the maximum number of iterations used by parameter learning to score each configuration.
getMaximumIterations() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets the maximum number of iterations used by parameter learning to score each configuration.
getMaximumIterations() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets the optional maximum number of iterations (moves) made during the search procedure.
getMaximumLevels() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets the maximum number of levels generated by the algorithm.
getMaximumSupport() - Method in class com.bayesserver.learning.parameters.InitializationOptions
Limits the amount of support each distribution is given during initialization.
getMaximumTemporalOrder() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets the maximum order of temporal links that are considered during learning.
getMaxTemporalOrder() - Method in class com.bayesserver.NodeDistributions
Gets the current maximum temporal order.
getMaxTime(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the maximum time containing evidence for a variable.
getMaxTime() - Method in class com.bayesserver.inference.DefaultEvidence
Gets the maximum time containing evidence.
getMaxTime(Variable) - Method in interface com.bayesserver.inference.Evidence
Gets the maximum time containing evidence for a variable.
getMaxTime() - Method in interface com.bayesserver.inference.Evidence
Gets the maximum time containing evidence.
getMaxValue() - Method in class com.bayesserver.Table
Gets the maximum table value, and the index at which it occurs.
getMean(int, int) - Method in class com.bayesserver.CLGaussian
Gets the mean of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getMean(Variable, State...) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable) - Method in class com.bayesserver.CLGaussian
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
getMean(Variable, Integer) - Method in class com.bayesserver.CLGaussian
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
getMean(Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(VariableContext, State...) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
getMean(Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
getMean(VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
getMeanAbsoluteError() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the mean absolute error (MAE), which is a common measure used to determine how close predictions are to the actual values.
getMeanActual() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the mean of the actual column.
getMeanActual() - Method in class com.bayesserver.data.R2CrossValidationTestResult
Gets the mean of the actual column values (as opposed to the predicted values).
getMeanSquaredError() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the mean squared error (MSE), which is a common measure used to determine how close predictions are to the actual values.
getMessage() - Method in interface com.bayesserver.data.discovery.DiscretizeProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.data.discovery.VariableGeneratorProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.PCStructuralLearningProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningProgressInfo
Gets a progress message.
getMessage() - Method in interface com.bayesserver.learning.structure.StructuralLearningProgressInfo
Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.TANStructuralLearningProgressInfo
Gets a progress message.
getMethod() - Method in class com.bayesserver.data.DataPartitioning
Gets the partitioning method.
getMethod() - Method in class com.bayesserver.learning.parameters.InitializationOptions
Determines the algorithm used for initialization.
getMethod() - Method in class com.bayesserver.learning.structure.LinkConstraint
Gets the method used to constrain nodes LinkConstraint.getA() and LinkConstraint.getB().
getMinimum() - Method in class com.bayesserver.Interval
Gets the minimum interval value.
getMinimumEndPoint() - Method in class com.bayesserver.Interval
Gets the end point type for the minimum value of the interval.
getMissing() - Method in class com.bayesserver.data.discovery.VariableInfoCounts
Gets the count of missing/null values.
getMissingDataExclusions() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
Variables can be added, to indicate that they should not generate missing values.
getMissingDataProbability() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
When positive, sets a certain percentage of values to missing (except when DataSamplingOptions.getMissingDataProbabilityMin() has a value).
getMissingDataProbabilityMin() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
When set, the missing data probability for each case varies randomly between DataSamplingOptions.getMissingDataProbabilityMin() and DataSamplingOptions.getMissingDataProbability().
getMissingProbability() - Method in class com.bayesserver.data.discovery.VariableInfo
Gets weighted and unweighted values between 0 and 1 indicating the percentage of data that is missing for this variable.
getMonitoredDistribution(Node) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
Gets a copy of the current distribution assigned to the [node].
getMonitoredDistribution(Node, Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
Gets a copy of the current distribution assigned to the [node] at the requested order.
getMonitoredDistribution(Node, NodeDistributionKey) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
Gets a copy of the current distribution assigned to the [node] at the requested order.
getMonitorLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Calculates the log likelihood at each iteration.
getMutualInformation() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets the mutual information between X and Y, denoted I(X;Y).
getMutualInformation() - Method in class com.bayesserver.learning.structure.FeatureSelectionOptions
Gets a value which when true calculates the mutual information between each target and test.
getMutualInformation() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
Gets the mutual information between the target and the test in NATS.
getName() - Method in class com.bayesserver.CustomProperty
Gets the name, which must be unique per CustomPropertyCollection.
getName() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets the name for the new variable.
getName() - Method in class com.bayesserver.learning.parameters.DistributerContext
Gets the name of the process/iteration being distributed.
getName() - Method in class com.bayesserver.Network
An optional name for the Bayesian network.
getName() - Method in class com.bayesserver.Node
The name of the node.
getName() - Method in class com.bayesserver.NodeGroup
Gets the name, which must be unique per NetworkNodeGroupCollection.
getName() - Method in class com.bayesserver.State
Gets the name of the state.
getName() - Method in class com.bayesserver.Variable
Gets the name of the variable.
getNestedTableCount() - Method in class com.bayesserver.data.DefaultDataReader
Gets the number of nested tables.
getNetwork() - Method in interface com.bayesserver.data.CrossValidationNetwork
Gets the network learnt from the cross validation partitioning.
getNetwork() - Method in class com.bayesserver.data.DefaultCrossValidationNetwork
Gets the network learnt from a cross validation partitioning.
getNetwork() - Method in class com.bayesserver.data.sampling.DataSampler
Gets the Bayesian network or Dynamic Bayesian network that was used in the constructor.
getNetwork() - Method in class com.bayesserver.inference.DefaultEvidence
Gets the Bayesian network that is the the target of the evidence.
getNetwork() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
getNetwork() - Method in interface com.bayesserver.inference.Evidence
Gets the Bayesian network that is the the target of the evidence.
getNetwork() - Method in interface com.bayesserver.inference.Inference
The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.LoopyBeliefInference
The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.RelevanceTreeInference
The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.VariableEliminationInference
The target Bayesian network.
getNetwork() - Method in class com.bayesserver.learning.parameters.DistributedMapperContext
Gets the Network that is being learnt by the distributed process.
getNetwork() - Method in class com.bayesserver.learning.parameters.ParameterLearning
Returns the relevant network.
getNetwork() - Method in class com.bayesserver.Link
The Network the link belongs to.
getNetwork() - Method in class com.bayesserver.NetworkLinkCollection
Gets the Network the collection belongs to.
getNetwork() - Method in class com.bayesserver.NetworkNodeCollection
The Network the collection belongs to.
getNetwork() - Method in class com.bayesserver.NetworkNodeGroupCollection
Gets the network instance that these groups belong to.
getNetwork() - Method in class com.bayesserver.NetworkVariableCollection
The Network the collection belongs to.
getNetwork() - Method in class com.bayesserver.Node
The Network the node belongs to.
getNode() - Method in class com.bayesserver.analysis.DSeparationTestResult
The test node.
getNode() - Method in class com.bayesserver.analysis.ParameterReference
Gets the node whose distribution parameter is being referenced.
getNode() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Gets the Node this distribution specification refers to.
getNode() - Method in class com.bayesserver.NodeDistributionOptions
The node this instance belongs to.
getNode() - Method in class com.bayesserver.NodeDistributions
Gets the node that this instance belongs to.
getNode() - Method in class com.bayesserver.NodeGroupCollection
The Node the collection belongs to.
getNode() - Method in class com.bayesserver.NodeLinkCollection
Gets the Node to which the collection belongs to.
getNode() - Method in class com.bayesserver.NodeVariableCollection
The Node the collection belongs to.
getNode() - Method in class com.bayesserver.UnrollOutput.NodeTime
Gets the node.
getNode() - Method in class com.bayesserver.Variable
Gets the Node this instance belongs to, if any.
getNodeGroups() - Method in class com.bayesserver.Network
Gets groups which nodes can belong to.
getNodes() - Method in class com.bayesserver.Network
The collection of nodes in the Bayesian network.
getNodeWidthOverride() - Method in class com.bayesserver.DecomposeOptions
Gets a value that can be used to override the width of nodes, used when laying out new nodes.
getNodeWidthOverride() - Method in class com.bayesserver.UnrollOptions
Gets a value that can be used to override the width of nodes, used when laying out nodes.
getNoisyOrder() - Method in class com.bayesserver.Link
Gets a value which determines the nature of the causal effect between the parent node (from) and a noisy child node (to).
getNoisyType() - Method in class com.bayesserver.NodeDistributionOptions
Gets a value which identifies this node as a noisy node or not.
getNotMissing() - Method in class com.bayesserver.data.discovery.VariableInfoCounts
Gets the counts of values that are not missing/null values.
getObject(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getObject(int) - Method in interface com.bayesserver.data.DataRecord
Gets an Object representation for the value at the specified column.
getObject(int) - Method in class com.bayesserver.data.DataTableReader
 
getObject(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets an Object representation for the value at the specified column.
getOneMinusPValue() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
Gets a value which equals one minus the p-value returned from the statical independence test.
getOnExecuteReader() - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand
Gets a function that is called when a new reader is created.
getOptions() - Method in class com.bayesserver.data.discovery.DiscretizationColumn
Gets the discretization options for this column of data.
getOrder() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Gets the order of the distribution, for temporal nodes.
getOrder() - Method in class com.bayesserver.NodeDistributionKey
Gets the temporal order of the related node distribution.
getOriginalNetwork() - Method in class com.bayesserver.DecomposeOutput
Gets the original network, containing nodes with multiple variables.
getOriginalVariable(Variable) - Method in class com.bayesserver.DecomposeOutput
Maps a variable in the decomposed network to the equivalent variable in the original network.
getOuter() - Method in class com.bayesserver.CLGaussian
 
getOuter() - Method in interface com.bayesserver.Distribution
Returns the parent distribution, if this instance is aggregated by another distribution.
getOuter() - Method in class com.bayesserver.Table
 
getOwner() - Method in class com.bayesserver.CLGaussian
Gets the current owner, if assigned to a node.
getOwner() - Method in class com.bayesserver.CustomPropertyCollection
Gets the instance that these custom properties belong to.
getOwner() - Method in interface com.bayesserver.Distribution
Gets the current owner, if assigned to a node.
getOwner() - Method in class com.bayesserver.Table
Gets the current owner, if assigned to a node.
getPair() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets the pair (X, Y) that these association results are calculated for.
getPairOutputs() - Method in class com.bayesserver.analysis.AssociationOutput
Gets the output for each pair.
getParameterCount(Network) - Static method in class com.bayesserver.ParameterCounter
Gets the number of parameters in a Bayesian network.
getParameterCount(Network, ParameterCountOptions) - Static method in class com.bayesserver.ParameterCounter
Gets the number of parameters in a Bayesian network.
getParameterCount(Node, int) - Static method in class com.bayesserver.ParameterCounter
Gets the parameter count for an individual node distribution.
getParameterCount(Node, NodeDistributionKey) - Static method in class com.bayesserver.ParameterCounter
Gets the parameter count for an individual node distribution.
getParameterCountLong(Network) - Static method in class com.bayesserver.ParameterCounter
Gets the number of parameters in a Bayesian network.
getParameterCountLong(Network, ParameterCountOptions) - Static method in class com.bayesserver.ParameterCounter
Gets the number of parameters in a Bayesian network.
getParameterCountLong(Node, NodeDistributionKey) - Static method in class com.bayesserver.ParameterCounter
Gets the parameter count for an individual node distribution.
getParameterValue() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets the original value of the parameter being analyzed.
getParameterValue1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets the original value of the first parameter being analyzed (t1).
getParameterValue2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets the original value of the second parameter being analyzed (t2).
getParent() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the parent collection.
getParent() - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
Gets the parent variable output.
getParent() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
Gets the parent collection.
getParent() - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
Gets the main output.
getParent() - Method in class com.bayesserver.CustomProperty
Gets the parent collection, if set, otherwise null.
getParent() - Method in class com.bayesserver.NodeGroup
Gets the parent collection, if set, otherwise null.
getPartition() - Method in class com.bayesserver.data.CrossValidationOutput
Gets the zero based index of the partition.
getPartitionCount() - Method in class com.bayesserver.data.DataPartitioning
Gets the total number of partitions.
getPartitionCount() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets the number of partitions used by scoring functions that use cross validation.
getPartitionNumber() - Method in class com.bayesserver.data.DataPartitioning
Gets the zero based partition number.
getPartitions() - Method in class com.bayesserver.analysis.ClusterCountOptions
Gets the number of cross validation partitions to use.
getPartitions() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
Gets the number of cross validation partitions to use.
getPartitions() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets the number of cross validation partitions to use when scoring each cluster count.
getPartitions() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets the number of cross validation partitions to use when scoring each cluster count.
getPoints() - Method in class com.bayesserver.analysis.LiftChart
Gets the xy points that make up the lift chart.
getPredictedProbability() - Method in class com.bayesserver.analysis.LiftChart
Gets the name of the data column which contains the predicted probability generated by an inference query.
getPriors() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Contains parameters used to avoid boundary conditions during learning.
getProbability() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the probability of this state with no evidence set on the target state.
getProbability() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
Gets the overall probability for this cell.
getProbabilityGivenActual() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
Gets the probability for this cell, conditional on the actual counts.
getProbabilityGivenPredicted() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
Gets the probability for this cell, conditional on the predicted counts.
getProbabilityGivenTarget() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the probability of this state given the target state.
getProbabilityGivenTarget() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
Gets the distribution of this variable given the target.
getProbabilityHypothesisGivenEvidence() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets P(h|e).
getProbabilityHypothesisGivenEvidence() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
Gets P(h|e).
getProbabilityTargetGivenThis() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the probability of the target state given this state.
getProficiencyXGivenY() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets the proficiency (uncertainty coefficient) U(X|Y) = I(X;Y) / H(X).
getProficiencyYGivenX() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets the proficiency (uncertainty coefficient) U(Y|X) = I(X;Y) / H(Y).
getProgress() - Method in class com.bayesserver.data.discovery.Clustering
Gets an instance that receive progress notifications.
getProgress() - Method in interface com.bayesserver.data.discovery.Discretize
Gets an instance that receive progress notifications.
getProgress() - Method in class com.bayesserver.data.discovery.EqualFrequencies
Gets an instance that receive progress notifications.
getProgress() - Method in class com.bayesserver.data.discovery.EqualIntervals
Gets an instance that receive progress notifications.
getProgress() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets of sets the instance implementing VariableGeneratorProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets of sets the instance implementing ParameterLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getPropagation() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Gets the propagation method to be used during inference.
getPropagation() - Method in interface com.bayesserver.inference.QueryOptions
Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
Gets the propagation method to use during marginalization.
getQueryDistance() - Method in class com.bayesserver.inference.QueryDistribution
Gets a value indicating whether the distance should be calculated between the query calculated with base evidence (or no evidence), and the same query calculated with evidence.
getQueryDistributions() - Method in interface com.bayesserver.inference.Inference
Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.LoopyBeliefInference
Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.RelevanceTreeInference
Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.VariableEliminationInference
getQueryEvidenceMode() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in interface com.bayesserver.inference.QueryOptions
Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.TreeQueryOptions
Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Determines whether evidence is retracted for each query.
getQueryLogLikelihood() - Method in class com.bayesserver.inference.QueryDistribution
Determines whether or not to calculate the QueryDistribution.getLogLikelihood() specific to the evidence used to calculate this query.
getQueryTimeout() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Gets the timeout to be used when statements are executed.
getReadInfo() - Method in class com.bayesserver.data.DefaultEvidenceReader
Provides information about the last read of non temporal data.
getRecord() - Method in class com.bayesserver.data.NestedReadInfo
The current nested table record.
getRecord() - Method in class com.bayesserver.data.ReadInfo
The current record.
getRecord() - Method in class com.bayesserver.data.TemporalReadInfo
The current temporal record.
getRelatedNode() - Method in class com.bayesserver.NodeDistributionKey
Gets the parent of the noisy node this distribution refers to, or the noisy node itself to identify the leak distribution.
getRMSE() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the root mean squared error (RMSE).
getRoot() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets the root of the Chow-Liu tree.
getRoot() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets the root of the TAN tree.
getRow(int[]) - Method in class com.bayesserver.TableAccessor
Gets the TableAccessor row for the given states.
getRow() - Method in class com.bayesserver.TableIterator
Gets the current position of the iterator.
getRows() - Method in class com.bayesserver.data.DataTable
Gets the rows of data in the table.
getRSquared() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the R squared value (Coefficient of determination).
getSampleCount() - Method in class com.bayesserver.analysis.AutoInsightSamplingOptions
The number of samples used to approximate sufficient statistics, when exact inference is not possible.
getSampleCount() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets a value indicating how many samples cases to generate in order to approximate the current query.
getSampleCount() - Method in interface com.bayesserver.inference.QuerySamplingOptions
Gets a value indicating how many samples cases to generate in order to approximate the current query.
getSampling() - Method in class com.bayesserver.analysis.AutoInsightOptions
Options affecting sampling, when approximate inference is required.
getSamplingProbability() - Method in class com.bayesserver.learning.parameters.InitializationOptions
A value between 0 and 1 (inclusive) indicating what probability of cases to use for initialization.
getSaveHyperparameters() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets a value indicating whether hyperparameters (e.g.
getScore() - Method in class com.bayesserver.analysis.ClusterScore
The score achieved for this number of clusters.
getScore() - Method in class com.bayesserver.analysis.LiftChart
Gets the overall score, which is a positive or negative probability between 0 and 1 indicating the classification performance of the network.
getScore() - Method in interface com.bayesserver.data.CrossValidationScore
Gets the combined score over each cross validation partitioning.
getScore() - Method in class com.bayesserver.data.DefaultCrossValidationScore
Gets the combined score over each cross validation partitioning.
getScoreMethod() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets the scoring method used to evaluate search moves.
getScores() - Method in class com.bayesserver.analysis.ClusterCountOutput
A list of scores, one for each cluster count in the same order passed to ClusterCount.
getSeed() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets an optional seed for the random number generator.
getSeed() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Gets the seed used by the random number generator.
getSeed() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the seed used to generate random numbers for initialization.
getSeed() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the seed used to generate random numbers for initialization.
getSensitivityValue() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
Gets the sensitivity value which is the derivative of the sensitivity function evaluated at t.
getSequenceLength() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
The sequence length generated for each sample from networks with temporal nodes.
getShift() - Method in class com.bayesserver.data.timeseries.WindowOptions
Gets the number of records between successive windows.
getSignificanceLevel() - Method in class com.bayesserver.learning.structure.IndependenceOptions
Gets the significance level used to accept or reject (conditional) independence tests.
getSimpleVariance() - Method in class com.bayesserver.learning.parameters.Priors
Used to make a fixed adjustment to all covariance matrices during learning, by increasing each diagonal (variance) entry.
getSliceCount() - Method in class com.bayesserver.UnrollOutput
Gets the slice count of the unrolled network.
getSliceGap() - Method in class com.bayesserver.UnrollOptions
Gets the gap between time slices.
getSortedContinuousHead() - Method in class com.bayesserver.CLGaussian
Gets the collection of continuous head variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedContinuousTail() - Method in class com.bayesserver.CLGaussian
Gets the collection of continuous tail variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedIndex(State...) - Method in class com.bayesserver.Table
Gets the index of the table element that corresponds to a particular combination of states.
getSortedIndex(StateContext...) - Method in class com.bayesserver.Table
Gets the index of the table element that corresponds to a particular combination of states and their times.
getSortedUniqueValues() - Method in class com.bayesserver.analysis.ConfusionMatrix
Gets a sorted list of unique values which is the union of the different actual and predicted values found.
getSortedVariables() - Method in class com.bayesserver.CLGaussian
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedVariables() - Method in interface com.bayesserver.Distribution
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedVariables() - Method in class com.bayesserver.Table
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortOrder() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets the sort order for states of a new discrete variable.
getState() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
Gets the state this insight refers to.
getState(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable) - Method in interface com.bayesserver.inference.Evidence
Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node) - Method in interface com.bayesserver.inference.Evidence
Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState() - Method in class com.bayesserver.StateContext
Gets the State.
getState(int, int) - Method in class com.bayesserver.TableAccessor
Gets the state at the given position [i] for the node given by [node].
getState(int) - Method in class com.bayesserver.TableIterator
Gets the state for an individual node indexed by the order of nodes in the TableIterator.
getStateAllDiffThis() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the probability of the hypothesis state (if specified) with all evidence to analyze minus the state probability for this evidence configuration.
getStateAllLiftThis() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the probability of the hypothesis state (if specified) when all evidence to analyze is set relative to when this evidence configuration is set.
getStateNotFoundAction() - Method in class com.bayesserver.data.VariableReference
Determines the action to take if the name or value from the data cannot be matched to a particular state for this reference variable.
getStateOutputs() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
Gets the insight for each state of this test variable.
getStateProbability() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the probability of the hypothesis state (if specified) for this output item evidence.
getStateProbabilityAll() - Method in class com.bayesserver.analysis.ImpactHypothesisOutput
Gets the probability of the hypothesis state (if any) with all evidence to analyze set.
getStateProbabilityNone() - Method in class com.bayesserver.analysis.ImpactHypothesisOutput
Gets the probability of the hypothesis state (if any) with no evidence to analyze set.
getStates() - Method in class com.bayesserver.analysis.ParameterReference
Gets the states which together locate a specific parameter in the node's distribution.
getStates(Variable, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
Fills out a buffer containing the soft evidence for a particular variable.
getStates(Node, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
Fills out a buffer containing the soft evidence for a node with a single variable.
getStates(Table) - Method in class com.bayesserver.inference.DefaultEvidence
Fills out a table containing the soft evidence for a particular variable.
getStates(Node, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
getStates(Variable, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Fills out a buffer containing the soft evidence for a particular variable at a specified time.
getStates(Variable, double[]) - Method in interface com.bayesserver.inference.Evidence
Fills out a buffer containing the soft evidence for a particular variable.
getStates(Node, double[]) - Method in interface com.bayesserver.inference.Evidence
Fills out a buffer containing the soft evidence for a node with a single variable.
getStates(Variable, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
Fills out a buffer containing the soft evidence for a particular variable at a specified time.
getStates(Node, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
getStates(Table) - Method in interface com.bayesserver.inference.Evidence
Fills out a table containing the soft evidence for a particular variable.
getStates() - Method in class com.bayesserver.State
Gets the StateCollection the state belongs to, if any.
getStates(int, int[]) - Method in class com.bayesserver.TableAccessor
Gets the states at the given position [i].
getStates(int[]) - Method in class com.bayesserver.TableIterator
Gets the states of all nodes, based on the order of nodes in the TableIterator not the underlying Table.
getStates() - Method in class com.bayesserver.Variable
Returns the collection of states belonging to the variable.
getStateThisDiffNone() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the probability of the hypothesis state (if specified) for this evidence configuration minus the state probability with no evidence to analyze.
getStateThisLiftNone() - Method in class com.bayesserver.analysis.ImpactOutputItem
Gets the probability of the hypothesis state (if specified) for this evidence configuration relative to when no evidence to analyze is set.
getStateValueType() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets the StateValueType for the new variable.
getStateValueType() - Method in class com.bayesserver.Variable
Gets the type of value that states belonging to this variable can represent.
getStop() - Method in interface com.bayesserver.Stop
When true, indicates to the algorithm to complete early.
getStopping() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets the instance implementing Stop used for early stopping.
getString(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
getString(int) - Method in interface com.bayesserver.data.DataRecord
Gets a string value for the specified column.
getString(int) - Method in class com.bayesserver.data.DataTableReader
 
getString(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Gets a string value for the specified column.
getSubsetMethod() - Method in class com.bayesserver.analysis.ImpactOptions
Gets a value which determines whether evidence subsets are included, excluded or both.
getSubsetMethod() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Gets a value which determines whether evidence subsets are included, excluded or both.
getSuggestedBinCount() - Method in class com.bayesserver.analysis.HistogramDensityOptions
Gets the approximate number of bins to use to represent the approximate density function.
getSuggestedBinCount() - Method in class com.bayesserver.data.discovery.DiscretizationOptions
Gets the number of suggested bins to use during discretization.
getSumSquaredError() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the sum of squared errors (SSE), which is a common measure used to determine how close predictions are to the actual values.
getSupport() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the support/weight for the values used to calculate the statistics.
getSymmetricMutualInformation() - Method in class com.bayesserver.analysis.AssociationPairOutput
Gets a normalized version of the mutual information called the 'symmetric uncertainty' between X and Y.
getSyncNodeVariableName() - Static method in class com.bayesserver.Network
When true synchronizes Variable names with their containing Node.
getTable() - Method in class com.bayesserver.CLGaussian
Gets the Table which specifies the distribution over any discrete variables.
getTable() - Method in class com.bayesserver.data.DataColumn
 
getTable() - Method in class com.bayesserver.data.DataRow
 
getTable() - Method in interface com.bayesserver.Distribution
Gets the Table which specifies the distribution over any discrete variables.
getTable() - Method in class com.bayesserver.Table
 
getTable() - Method in class com.bayesserver.TableAccessor
Gets the underlying Table.
getTable() - Method in class com.bayesserver.TableIterator
Gets the underlying Table.
getTableIndex(int) - Method in class com.bayesserver.TableAccessor
Gets the equivalent index in the underlying table that corresponds to the index in the accessor.
getTableRow() - Method in class com.bayesserver.TableIterator
Gets the position of the iterator in the underlying Table.
getTarget() - Method in class com.bayesserver.analysis.AutoInsightOutput
Gets the target state used to calculate the insight.
getTarget() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
Gets the variable that was the target of the feature selection test.
getTarget() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets the target of the TAN tree.
getTargetProbability() - Method in class com.bayesserver.analysis.AutoInsightOutput
Gets the probability of the target state, given any optional background evidence.
getTargetValue() - Method in class com.bayesserver.analysis.LiftChart
Gets the target value which we are interested in.
getTemporalOrder() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
Gets the temporal order (if any) used to test the variables.
getTemporalOrder() - Method in class com.bayesserver.learning.structure.LinkConstraint
Gets the temporal order of the constraint.
getTemporalOrder() - Method in class com.bayesserver.Link
Gets the temporal order of the link.
getTemporalOrder() - Method in class com.bayesserver.NodeDistributions.DistributionOrder
Gets the temporal order of the distribution.
getTemporalReadInfo() - Method in class com.bayesserver.data.DefaultEvidenceReader
Provides information about the last read of temporal data.
getTemporalType() - Method in class com.bayesserver.Node
The TemporalType of the node.
getTerminalTime() - Method in class com.bayesserver.analysis.DSeparationOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.analysis.ValueOfInformationOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in interface com.bayesserver.inference.QueryOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTestIndependence() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets a value which when true uses independence tests to reduce the search space.
getTestOutputs() - Method in class com.bayesserver.analysis.ValueOfInformationOutput
Gets the result of tests carried out on the test variables.
getTestPartitioning() - Method in class com.bayesserver.data.CrossValidationOutput
Gets the test DataPartitioning associated with this partition.
getTestResults() - Method in class com.bayesserver.analysis.DSeparationOutput
The collection of test results.
getTests() - Method in class com.bayesserver.learning.structure.FeatureSelectionOutput
Gets the tests carried out for each variable against the target.
getTestSingleCluster() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets a value which determines whether a test is performed for a single cluster (i.e.
getTime() - Method in class com.bayesserver.analysis.DSeparationTestResult
The zero based time at which the test was performed, or null if the node is not temporal.
getTime() - Method in class com.bayesserver.StateContext
Gets the zero based time associated with the state if the state belongs to a temporal node, or null otherwise.
getTime() - Method in class com.bayesserver.UnrollOutput.NodeTime
Gets the time of the node, or null if a time is not appropriate for the temporal type of the node.
getTime() - Method in class com.bayesserver.UnrollOutput.VariableTime
Gets the time of the variable, or null if a time is not appropriate for the temporal type of the variable.
getTime() - Method in class com.bayesserver.VariableContext
Gets the time associated with the variable if it belongs to a temporal node.
getTimeColumn() - Method in class com.bayesserver.data.TemporalReaderOptions
The name of the time column in the temporal data, if temporal data is present.
getTimeIndex() - Method in class com.bayesserver.data.TemporalReadInfo
Gets the zero based time index (e.g.
getTimes() - Method in class com.bayesserver.data.timeseries.WindowOptions
Gets the times to include in the window.
getTimeSeriesMode() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the mode in which time series distributions are learned.
getTimeValue() - Method in class com.bayesserver.data.TemporalReadInfo
Gets the current value in the time column.
getTimeValueType() - Method in class com.bayesserver.data.TemporalReaderOptions
The type of values contained in the time column.
getTo() - Method in class com.bayesserver.Link
The child node of the directed link.
getTolerance() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Gets the tolerance used to determine whether or not the approximate inference process has converged.
getTolerance() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets the tolerance used to determine whether or not parameter learning has converged.
getTolerance() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets the tolerance used to determine whether or not a search move is a significant improvement.
getToleranceOrDefault() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
If Tolerance is null, this returns the default tolerance for the given convergence method, otherwise Tolerance is returned.
getToleranceOrDefault() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
If Tolerance is null, this returns the default tolerance for the given scoring method, otherwise Tolerance is returned.
getTotalCount() - Method in class com.bayesserver.analysis.ConfusionMatrix
Gets a count of the number of predictions, whether they were correct or not.
getTrainingPartioning() - Method in class com.bayesserver.data.CrossValidationOutput
Gets the training DataPartitioning associated with this partition.
getTreeWidth() - Method in class com.bayesserver.inference.TreeQueryOptions
Gets a value indicating whether or not to calculate the tree width.
getTreeWidth() - Method in class com.bayesserver.inference.TreeQueryOutput
Gets the tree width, if requested.
getUnrolled() - Method in class com.bayesserver.UnrollOutput
Gets the unrolled Dynamic Bayesian network.
getUnrolledNode(Node, Integer) - Method in class com.bayesserver.UnrollOutput
Maps between a node in the original Dynamic Bayesian network, and the corresponding node in the unrolled network.
getUnrolledVariable(Variable, Integer) - Method in class com.bayesserver.UnrollOutput
Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
getUnweighted() - Method in class com.bayesserver.data.discovery.VariableInfoCount
The number of records.
getUnweighted() - Method in class com.bayesserver.data.discovery.VariableInfoValue
Gets the unweighted value.
getUnweightedCaseCount() - Method in class com.bayesserver.data.DataCountOutput
Gets the number of cases that match the given evidence.
getUnweightedCaseCount() - Method in class com.bayesserver.data.DataProgressEventArgs
Gets the number of cases read so far.
getUnweightedCaseCount() - Method in class com.bayesserver.data.DefaultEvidenceReader
Gets the number of cases (unweighted) read so far.
getUnweightedCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the unweighted case count in the learning data.
getUnweightedTemporalCount() - Method in class com.bayesserver.data.DataProgressEventArgs
Gets the number of temporal rows read so far for all cases.
getValue() - Method in class com.bayesserver.CustomProperty
The custom property value.
getValue() - Method in interface com.bayesserver.data.CrossValidationTestResult
Gets the test result value for this test partitioning.
getValue() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
Gets the test result value for this test partitioning.
getValue() - Method in class com.bayesserver.data.discovery.WeightedValue
Gets the value, which can be null.
getValue() - Method in class com.bayesserver.data.R2CrossValidationTestResult
Gets the test result value for this test partitioning.
getValue() - Method in class com.bayesserver.State
Gets an optional value for a state, such as an interval for discretized variables.
getValue() - Method in class com.bayesserver.Table.MaxValue
 
getValue() - Method in class com.bayesserver.TableIterator
Gets the underlying Table value at the current position of the iterator.
getValueType() - Method in class com.bayesserver.data.discovery.VariableDefinition
Gets the VariableValueType for the new variable.
getValueType() - Method in class com.bayesserver.Variable
Gets the variable's value type, e.g.
getVariable() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
Gets the test variable.
getVariable() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
Gets the variable that was tested.
getVariable() - Method in class com.bayesserver.data.discovery.VariableInfo
Gets the generated Variable.
getVariable() - Method in class com.bayesserver.data.VariableReference
Gets the variable.
getVariable() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
Gets the variable which was tested to see if it is likely to be a feature of the FeatureSelectionTest.getTarget() variable.
getVariable() - Method in class com.bayesserver.State
Gets the Variable the state belongs to, if any.
getVariable() - Method in class com.bayesserver.StateCollection
Gets the Variable this collection belongs to.
getVariable() - Method in class com.bayesserver.UnrollOutput.VariableTime
Gets the variable.
getVariable() - Method in class com.bayesserver.VariableContext
Gets the variable.
getVariableOutputs() - Method in class com.bayesserver.analysis.AutoInsightOutput
Contains the insights from each test variable.
getVariables(Variable[]) - Method in class com.bayesserver.inference.DefaultEvidence
Fills out a buffer with all variables that have either hard or soft evidence.
getVariables(Variable[]) - Method in interface com.bayesserver.inference.Evidence
Fills out a buffer with all variables that have either hard or soft evidence.
getVariables() - Method in class com.bayesserver.Network
The collection of variables in the Bayesian network.
getVariables() - Method in class com.bayesserver.Node
Collection of variables represented by the node.
getVariance(int, int) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getVariance(Variable, State...) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable) - Method in class com.bayesserver.CLGaussian
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
getVariance(Variable, Integer) - Method in class com.bayesserver.CLGaussian
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
getVariance(Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(VariableContext, State...) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVarianceActual() - Method in class com.bayesserver.analysis.RegressionStatistics
Gets the variance of the actual column.
getVarianceActual() - Method in class com.bayesserver.data.R2CrossValidationTestResult
Gets the variance of the actual column values (as opposed to the predicted values).
getWeight(int, int, int) - Method in class com.bayesserver.CLGaussian
Gets the weight (regression coefficient) of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getWeight(Variable, Variable, State...) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Integer, Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(VariableContext, VariableContext, State...) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Variable) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
getWeight(Variable, Integer, Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Integer, Variable, Integer) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
getWeight(VariableContext, VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Integer, Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(VariableContext, VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight() - Method in class com.bayesserver.data.discovery.WeightedValue
Gets the weight (support) for the WeightedValue.getValue().
getWeight() - Method in class com.bayesserver.data.ReadInfo
The case weight.
getWeight() - Method in class com.bayesserver.inference.DefaultEvidence
Gets a weight that can be applied to the evidence.
getWeight() - Method in interface com.bayesserver.inference.Evidence
Gets a weight that can be applied to the evidence.
getWeightColumn() - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
Gets a column that contains case weights for each record.
getWeightColumn() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets the name of a column which contains a weight (support) for each case.
getWeightColumn() - Method in class com.bayesserver.data.ReaderOptions
The name of the case weight column, if one is present.
getWeighted() - Method in class com.bayesserver.data.discovery.VariableInfoCount
The sum of record weights.
getWeighted() - Method in class com.bayesserver.data.discovery.VariableInfoValue
Gets the weighted value.
getWeightedCaseCount() - Method in interface com.bayesserver.data.CrossValidationTestResult
Gets the number of records in the test partitioning.
getWeightedCaseCount() - Method in class com.bayesserver.data.DataCountOutput
Gets the number of cases that match the given evidence.
getWeightedCaseCount() - Method in class com.bayesserver.data.DataProgressEventArgs
Gets the number of cases read so far.
getWeightedCaseCount() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
Gets the number of records in the test partitioning.
getWeightedCaseCount() - Method in class com.bayesserver.data.DefaultEvidenceReader
Gets the number of cases (weighted) read so far.
getWeightedCaseCount() - Method in class com.bayesserver.data.R2CrossValidationTestResult
Gets the number of records in the test partitioning.
getWeightedCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
Gets the weighted case count in the learning data.
getWidth() - Method in class com.bayesserver.Bounds
Gets the width of the element.
getWindowColumnName() - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
Gets the name of the column which will contain the window identifier.
getWindowTimeColumnName() - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
Gets the name of the column which will contain the window time.
getX() - Method in class com.bayesserver.analysis.AssociationPair
Gets the variable contexts in the first set.
getX() - Method in class com.bayesserver.analysis.LiftChartPoint
Gets the value on the x-axis.
getX() - Method in class com.bayesserver.Bounds
Gets the x-axis value of the left side of the element.
getY() - Method in class com.bayesserver.analysis.AssociationPair
Gets the varible contexts in the second set.
getY() - Method in class com.bayesserver.analysis.LiftChartPoint
Gets the value on the y-axis.
getY() - Method in class com.bayesserver.Bounds
Gets the y-axis value of the top side of the element.

H

hashCode() - Method in class com.bayesserver.Bounds
 
hashCode() - Method in class com.bayesserver.data.discovery.WeightedValue
 
hashCode() - Method in class com.bayesserver.Interval
 
hashCode() - Method in class com.bayesserver.NodeDistributionKey
hashCode() - Method in class com.bayesserver.StateContext
 
hashCode() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
 
hashCode() - Method in class com.bayesserver.ValidationOptions
 
HeadTail - Enum in com.bayesserver
Indicates whether a variable is marked as head or tail in a distribution.
HierarchicalLinkOutput - Class in com.bayesserver.learning.structure
Contains information about a new link learnt using the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning algorithm.
HierarchicalStructuralLearning - Class in com.bayesserver.learning.structure
A structural learning algorithm for Bayesian networks that groups subsets of nodes into a hierarchy.
HierarchicalStructuralLearning() - Constructor for class com.bayesserver.learning.structure.HierarchicalStructuralLearning
 
HierarchicalStructuralLearningOptions - Class in com.bayesserver.learning.structure
Options for structural learning with the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning class.
HierarchicalStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
 
HierarchicalStructuralLearningOutput - Class in com.bayesserver.learning.structure
Contains information returned from the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning algorithm.
HierarchicalStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
Progress information returned from the Hierarchical structural learning algorithm.
HistogramDensity - Class in com.bayesserver.analysis
Represents an empirical density function built from a histogram, which can represent arbitrary univariate distributions.
HistogramDensity(List<Interval<Double>>, List<Double>) - Constructor for class com.bayesserver.analysis.HistogramDensity
Constructs an empirical density function.
HistogramDensityOptions - Class in com.bayesserver.analysis
Options for learning a histogram based empirical density.
HistogramDensityOptions() - Constructor for class com.bayesserver.analysis.HistogramDensityOptions
 

I

Impact - Class in com.bayesserver.analysis
Analyzes the impact of evidence.
ImpactAction - Interface in com.bayesserver.analysis
ImpactHypothesisOutput - Class in com.bayesserver.analysis
Output information about the hypothesis variable/state from an Impact analysis.
ImpactOptions - Class in com.bayesserver.analysis
Options affecting how Impact analysis calculations are performed.
ImpactOptions() - Constructor for class com.bayesserver.analysis.ImpactOptions
 
ImpactOutput - Class in com.bayesserver.analysis
Contains the results of an Impact analysis.
ImpactOutputItem - Class in com.bayesserver.analysis
The output from an impact analysis, for a particular subset of evidence.
ImpactSubsetMethod - Enum in com.bayesserver.analysis
Determines how subsets are determined during impact analysis.
include(DataRecord) - Method in interface com.bayesserver.data.DataReaderFilter
Determines whether a record should be included or not.
include(DataRecord) - Method in class com.bayesserver.data.PartitionDataReaderFilter
Determines whether a record should be included or not.
InconsistentEvidenceException - Exception in com.bayesserver.inference
Exception raised when either inconsistent evidence is detected, or underflow has occurred.
InconsistentEvidenceException() - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException
Initializes a new instance of the InconsistentEvidenceException class.
InconsistentEvidenceException(String) - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException
Initializes a new instance of the InconsistentEvidenceException class.
InconsistentEvidenceException(String, Throwable) - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException
Initializes a new instance of the InconsistentEvidenceException class with a specified error message and a reference to the inner exception that is the cause of this exception.
InconsistentEvidenceException(Throwable) - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException
Initializes a new instance of the InconsistentEvidenceException class a reference to the inner exception that is the cause of this exception.
increment() - Method in class com.bayesserver.TableIterator
Moves the iterator to the next value, with respect to the TableIterator node order.
IndependenceOptions - Class in com.bayesserver.learning.structure
Options governing independence and conditional independence tests.
IndependenceOptions() - Constructor for class com.bayesserver.learning.structure.IndependenceOptions
 
indexOf(String) - Method in class com.bayesserver.data.DataColumnCollection
Gets the index of the column with the given name.
indexOf(Object) - Method in class com.bayesserver.NetworkLinkCollection
Determines the index of a specific Link in the collection.
indexOf(Object) - Method in class com.bayesserver.NetworkNodeCollection
Determines the index of a specific Node in the collection.
indexOf(Object) - Method in class com.bayesserver.NetworkVariableCollection
Determines the index of a specific Variable in the collection.
indexOf(Object) - Method in class com.bayesserver.NodeGroupCollection
Determines the index of a specific group in the collection.
indexOf(Object) - Method in class com.bayesserver.NodeVariableCollection
Determines the index of a specific Variable in the collection.
indexOf(Object) - Method in class com.bayesserver.StateCollection
Determines the index of a specific State in the collection.
indexOf(Variable) - Method in class com.bayesserver.VariableContextCollection
Determines the index of a specific Variable in the collection.
indexOf(VariableContext, boolean) - Method in class com.bayesserver.VariableContextCollection
Determines the index of a specific variable-time combination in the collection.
indexOf(Variable, Integer) - Method in class com.bayesserver.VariableContextCollection
Determines the index of a specific Variable in the collection at the specified [time].
Inference - Interface in com.bayesserver.inference
The interface for a Bayesian network inference algorithm, which is used to perform queries such as calculating posterior probabilities and log-likelihood values for a case.
InferenceFactory - Interface in com.bayesserver.inference
Uses the factory design pattern to create inference related objects for inference algorithms.
InitializationMethod - Enum in com.bayesserver.learning.parameters
Determines the algorithm used to initialize distributions during parameter learning.
InitializationOptions - Class in com.bayesserver.learning.parameters
Options governing the initialization of distributions at the start of parameter learning.
InSampleAnomalyDetection - Class in com.bayesserver.analysis
Detects in-sample anomalies in a data set.
InSampleAnomalyDetectionActions - Interface in com.bayesserver.analysis
Actions which the caller must implement to use InSampleAnomalyDetection.
InSampleAnomalyDetectionOptions - Class in com.bayesserver.analysis
Options used by InSampleAnomalyDetection.
InSampleAnomalyDetectionOptions() - Constructor for class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
 
InSampleAnomalyDetectionOutput - Class in com.bayesserver.analysis
Output used by InSampleAnomalyDetection.
instantiate(Double[]) - Method in class com.bayesserver.CLGaussian
Calculates the distribution which results from instantiating a number of variables.
instantiate(Variable, double) - Method in class com.bayesserver.CLGaussian
Calculates the distribution which results from instantiating a particular variable.
instantiate(Variable, double, Integer) - Method in class com.bayesserver.CLGaussian
Calculates the distribution which results from instantiating a particular variable at a specified time.
instantiate(Double[]) - Method in interface com.bayesserver.Distribution
Calculates the distribution which results from instantiating a number of variables.
instantiate(Double[]) - Method in class com.bayesserver.Table
Creates a table with a subset of variables by setting hard evidence on one or more variables.
instantiate(Integer[]) - Method in class com.bayesserver.Table
Creates a table with a subset of variables by setting hard evidence on one or more variables.
instantiateDiscrete(Integer[]) - Method in class com.bayesserver.CLGaussian
Instantiates discrete variables.
instantiateHead(Variable, double, Integer) - Method in class com.bayesserver.CLGaussian
Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
instantiateHead(Variable, double, Integer, double[]) - Method in class com.bayesserver.CLGaussian
Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
instantiateHead(double[], double[]) - Method in class com.bayesserver.CLGaussian
Instantiates all continuous head variable contexts.
instantiateHeads(Double[], double[]) - Method in class com.bayesserver.CLGaussian
Instantiates continuous head variable contexts.
instantiateTails(Double[]) - Method in class com.bayesserver.CLGaussian
Calculates the distribution which results from instantiating continuous tail variables.
Interval<T extends Comparable> - Class in com.bayesserver
An interval, defined by a minimum and maximum with respective open or closed endpoints.
Interval(T, T, IntervalEndPoint, IntervalEndPoint) - Constructor for class com.bayesserver.Interval
Initializes a new instance of an Interval.
Interval() - Constructor for class com.bayesserver.Interval
 
IntervalEndPoint - Enum in com.bayesserver
The type of end point for an interval.
invalidate() - Static method in class com.bayesserver.License
Resets any previous validation.
InvalidNetworkException - Exception in com.bayesserver
Raised when a network has not been correctly specified.
InvalidNetworkException() - Constructor for exception com.bayesserver.InvalidNetworkException
Initializes a new instance of the InvalidNetworkException class.
InvalidNetworkException(String) - Constructor for exception com.bayesserver.InvalidNetworkException
Initializes a new instance of the InvalidNetworkException class with a specified error message.
InvalidNetworkException(String, Throwable) - Constructor for exception com.bayesserver.InvalidNetworkException
Initializes a new instance of the InvalidNetworkException class with a specified error message and a reference to the inner exception that is the cause of this exception.
InvalidNetworkException(Throwable) - Constructor for exception com.bayesserver.InvalidNetworkException
Initializes a new instance of the InvalidNetworkException class with a reference to the inner exception that is the cause of this exception.
inverseCdf(double) - Method in interface com.bayesserver.analysis.EmpiricalDensity
Calculates an approximate value for the inverse cumulative distribution function.
inverseCdf(double) - Method in class com.bayesserver.analysis.HistogramDensity
Calculates an approximate value for the inverse cumulative distribution function.
isAnomalous() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput
Determines whether the record is deemed anomalous.
isAnomalous(Double) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput
Determines whether the record is deemed anomalous.
isDag(Network) - Static method in class com.bayesserver.Dag
Determines if a network is a Directed Acyclic Graph (DAG).
isDag(Network, Iterable<Link>, Iterable<Link>) - Static method in class com.bayesserver.Dag
Determines if a network is a DAG (Directed Acyclic Graph).
isDag() - Method in class com.bayesserver.Network
Determines whether this instance is a Directed Acyclic Graph (DAG) which is a requirement for Bayesian networks.
isFeature(double) - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
Provides a hint as to whether the variable is likely to be a feature or not, at the given [significanceLevel].
isHead() - Method in class com.bayesserver.VariableContext
Determines whether this instance is marked as Head.
isNull(int) - Method in class com.bayesserver.data.DataReaderFiltered
 
isNull(int) - Method in interface com.bayesserver.data.DataRecord
Determines whether the value is null (missing) for the specified column.
isNull(int) - Method in class com.bayesserver.data.DataTableReader
 
isNull(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
Determines whether the value is null (missing) for the specified column.
isReadOnly() - Method in class com.bayesserver.CLGaussian
Indicates whether the distribution is read only.
isReadOnly() - Method in interface com.bayesserver.Distribution
Indicates whether the distribution is read only.
isReadOnly() - Method in class com.bayesserver.Table
Indicates whether the distribution is read only.
isTail() - Method in class com.bayesserver.VariableContext
Determines whether this instance is marked as Tail.
isTree() - Method in class com.bayesserver.Network
Determines whether this instance is a tree (singly connected).
iterate(VariableContextCollection[], int[], MultipleIterator.Combination) - Static method in class com.bayesserver.MultipleIterator
Iterates over all the variables and their states found in [subsets].
iterate(VariableContextCollection, VariableContextCollection[], int[], MultipleIterator.Combination) - Static method in class com.bayesserver.MultipleIterator
Iterates over all the variables and their states found in [subsets].

J

JensenShannon - Class in com.bayesserver.statistics
Methods for computing the Jensen Shannon divergence, which measures the similarity between probability distributions.

K

kFold(int, int, CrossValidationActions) - Static method in class com.bayesserver.data.CrossValidation
Performs k-fold cross validation.
kFoldList(int) - Static method in class com.bayesserver.data.CrossValidation
Gets a list of training and test DataPartitioning instances for each partition.
KullbackLeibler - Class in com.bayesserver.statistics
Calculate the KullbackÔÇôLeibler divergence between 2 distributions with the same variables, D(P||Q).

L

learn(Network, EvidenceReaderCommand) - Method in interface com.bayesserver.analysis.ClusterCountActions
A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.
learn(Iterable<WeightedValue>, HistogramDensityOptions) - Static method in class com.bayesserver.analysis.HistogramDensity
Learns a univariate empirical density from data.
learn(Network, EvidenceReaderCommandFactory, InSampleAnomalyDetectionActions, InSampleAnomalyDetectionOptions) - Static method in class com.bayesserver.analysis.InSampleAnomalyDetection
Build the in-sample anomaly detector, which can be used to remove anomalous data from a training data set.
learn(Network, EvidenceReaderCommand) - Method in interface com.bayesserver.analysis.InSampleAnomalyDetectionActions
A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.
learn(DataPartitioning) - Method in interface com.bayesserver.data.CrossValidationActions
A user supplied function to learn a network based on a training partitioning of the data.
learn(EvidenceReaderCommand, ParameterLearningOptions) - Method in class com.bayesserver.learning.parameters.ParameterLearning
Learns the parameters of a Bayesian network or Dynamic Bayesian network, from data.
learn(EvidenceReaderCommand, List<DistributionSpecification>, ParameterLearningOptions) - Method in class com.bayesserver.learning.parameters.ParameterLearning
Learns the parameters of a Bayesian network or Dynamic Bayesian network, from data.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearning
Learn a cluster / mixture model.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.PCStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.SearchStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.SearchStructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in interface com.bayesserver.learning.structure.StructuralLearning
Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.TANStructuralLearning
Learn the structure (links) of a Bayesian network.
learnDistributed(Network, ParameterLearningOptions, Distributer<DistributerContext>) - Static method in class com.bayesserver.learning.parameters.ParameterLearning
Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
learnDistributed(Network, List<DistributionSpecification>, ParameterLearningOptions, Distributer<DistributerContext>) - Static method in class com.bayesserver.learning.parameters.ParameterLearning
Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
learnDistributedMapper(EvidencePartition<DistributedMapperContext>, NameValuesReader, NameValuesWriter, InferenceFactory) - Static method in class com.bayesserver.learning.parameters.ParameterLearning
This method should be called during distributed parameter learning on a distributed partition.
learnDistributedReducer(Iterable<NameValuesReader>, NameValuesReader, NameValuesWriter) - Static method in class com.bayesserver.learning.parameters.ParameterLearning
License - Class in com.bayesserver
Provides license validation.
LiftChart - Class in com.bayesserver.analysis
Represents a lift chart, used to measure predictive performance.
LiftChartPoint - Class in com.bayesserver.analysis
Represents an XY coordinate in a lift chart.
LiftChartPoint() - Constructor for class com.bayesserver.analysis.LiftChartPoint
 
LikelihoodSamplingInference - Class in com.bayesserver.inference
An approximate probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, based on Likelihood Sampling.
LikelihoodSamplingInference(Network) - Constructor for class com.bayesserver.inference.LikelihoodSamplingInference
Initializes a new instance of the LikelihoodSamplingInference class, with the target Bayesian network.
LikelihoodSamplingInferenceFactory - Class in com.bayesserver.inference
Uses the factory design pattern to create inference related objects for the Likelihood Sampling algorithm.
LikelihoodSamplingInferenceFactory() - Constructor for class com.bayesserver.inference.LikelihoodSamplingInferenceFactory
 
LikelihoodSamplingQueryOptions - Class in com.bayesserver.inference
LikelihoodSamplingQueryOptions() - Constructor for class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Initializes a new instance of the LikelihoodSamplingQueryOptions class.
LikelihoodSamplingQueryOutput - Class in com.bayesserver.inference
Returns any information, in addition to the distributions, that is requested from a query.
LikelihoodSamplingQueryOutput() - Constructor for class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Initializes a new instance of the LikelihoodSamplingQueryOutput class.
Link - Class in com.bayesserver
Represents a directed link in a Bayesian network.
Link(Node, Node) - Constructor for class com.bayesserver.Link
Initializes a new instance of the Link class with the parent node specified in [from] and the child in [to].
Link(Node, Node, int) - Constructor for class com.bayesserver.Link
Initializes a new instance of the Link class with a specified [temporalOrder], the parent node specified in [from] and the child in [to].
linkCollectionChange(int, Link, Link, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor
For internal use.
LinkConstraint - Class in com.bayesserver.learning.structure
Defines a constraint on a link between two nodes during structural learning.
LinkConstraint(Node, Node, LinkConstraintMethod, LinkConstraintFailureMode) - Constructor for class com.bayesserver.learning.structure.LinkConstraint
Initializes a new instance of the LinkConstraint class.
LinkConstraint(Node, Node, Integer, LinkConstraintMethod, LinkConstraintFailureMode) - Constructor for class com.bayesserver.learning.structure.LinkConstraint
Initializes a new instance of the LinkConstraint class.
LinkConstraintCollection - Class in com.bayesserver.learning.structure
A collection of link constraints.
LinkConstraintCollection() - Constructor for class com.bayesserver.learning.structure.LinkConstraintCollection
 
LinkConstraintFailureMode - Enum in com.bayesserver.learning.structure
Determines the action taken if a link constraint cannot be honoured.
LinkConstraintMethod - Enum in com.bayesserver.learning.structure
Determines how a link is constrained.
LinkOutput - Interface in com.bayesserver.learning.structure
Contains information about a link returned from a structural learning algorithm.
load(InputStream) - Method in class com.bayesserver.inference.DefaultEvidence
Loads evidence from the specified stream.
load(String) - Method in class com.bayesserver.inference.DefaultEvidence
Loads evidence from the specified file.
load(InputStream) - Method in interface com.bayesserver.inference.Evidence
Loads evidence from the specified stream.
load(String) - Method in interface com.bayesserver.inference.Evidence
Loads evidence from the specified file.
load(String) - Method in class com.bayesserver.Network
Loads a Network from the specified [path].
load(InputStream) - Method in class com.bayesserver.Network
Loads a Network from the specified input InputStream.
loadFromString(String, String) - Method in class com.bayesserver.Network
Loads a network from a string using the specified encoding.
loadFromString(String) - Method in class com.bayesserver.Network
Loads a network from a string using UTF-8 encoding.
LogarithmBase - Enum in com.bayesserver.statistics
Determines the base of the logarithm to use during calculations such as mutual information.
LogLikelihoodAnalysis - Class in com.bayesserver.analysis
Analyzes the log-likelihood for different evidence subsets.
LogLikelihoodAnalysisAction - Interface in com.bayesserver.analysis
LogLikelihoodAnalysisBaselineOutput - Class in com.bayesserver.analysis
Output information about the log-likelihood from a log-likelihood analysis.
LogLikelihoodAnalysisOptions - Class in com.bayesserver.analysis
Options affecting how Log-Likelihood analysis calculations are performed.
LogLikelihoodAnalysisOptions() - Constructor for class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
 
LogLikelihoodAnalysisOutput - Class in com.bayesserver.analysis
Contains the results of a Log-Likelihood analysis.
LogLikelihoodAnalysisOutputItem - Class in com.bayesserver.analysis
The output from a Log-Likelihood analysis, for a particular subset of evidence.
LogLikelihoodAnalysisSubsetMethod - Enum in com.bayesserver.analysis
Determines how subsets are determined during a Log-Likelihood analysis.
LoopyBeliefInference - Class in com.bayesserver.inference
An approximate but deterministic probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks based on Loopy Belief Propagation.
LoopyBeliefInference(Network) - Constructor for class com.bayesserver.inference.LoopyBeliefInference
Initializes a new instance of the LoopyBeliefInference class, with the target Bayesian network.
LoopyBeliefInferenceFactory - Class in com.bayesserver.inference
Uses the factory design pattern to create inference related objects for the Loopy Belief algorithm.
LoopyBeliefInferenceFactory() - Constructor for class com.bayesserver.inference.LoopyBeliefInferenceFactory
 
LoopyBeliefQueryOptions - Class in com.bayesserver.inference
LoopyBeliefQueryOptions() - Constructor for class com.bayesserver.inference.LoopyBeliefQueryOptions
Initializes a new instance of the LoopyBeliefQueryOptions class.
LoopyBeliefQueryOutput - Class in com.bayesserver.inference
Returns any information, in addition to the distributions, that is requested from a query.
LoopyBeliefQueryOutput() - Constructor for class com.bayesserver.inference.LoopyBeliefQueryOutput
Initializes a new instance of the LoopyBeliefQueryOutput class.

M

marginalize(Distribution) - Method in class com.bayesserver.CLGaussian
Marginalizes (integrates) the [superset] into this instance.
marginalize(Distribution, PropagationMethod) - Method in class com.bayesserver.CLGaussian
Marginalizes (integrates) the [superset] into this instance.
marginalize(CLGaussian) - Method in class com.bayesserver.CLGaussian
Marginalizes (sums/integrates) the [superset] into this instance.
marginalize(Distribution) - Method in interface com.bayesserver.Distribution
Marginalizes (sums/integrates) the [superset] into this instance.
marginalize(Distribution, PropagationMethod) - Method in interface com.bayesserver.Distribution
Marginalizes (sums/integrates) the [superset] into this instance.
marginalize(Distribution) - Method in class com.bayesserver.Table
Marginalizes (sums) the [superset] into this instance.
marginalize(Distribution, PropagationMethod) - Method in class com.bayesserver.Table
Marginalizes (sums) the [superset] into this instance.
marginalize(Table) - Method in class com.bayesserver.Table
Marginalizes (sums) the [superset] into this instance.
marginalize(Table, PropagationMethod) - Method in class com.bayesserver.Table
Marginalizes (sums) the [superset] into this instance.
marginalize(Table, boolean) - Method in class com.bayesserver.Table
Marginalizes (sums) the [superset] into this instance.
marginalize(Table, boolean, PropagationMethod) - Method in class com.bayesserver.Table
Marginalizes (sums) the [superset] into this instance.
marginalizeLowMemory(Table[]) - Method in class com.bayesserver.Table
Marginalizes (sums) the combined [tables], without requiring the memory for the combined distribution.
marginalizeLowMemory(Table[], Table.MarginalizeLowMemoryOptions) - Method in class com.bayesserver.Table
Marginalizes (sums) the combined [tables], without requiring the memory for the combined distribution.
marginalizeTo(Table) - Method in class com.bayesserver.CLGaussian
Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
marginalizeTo(Table, PropagationMethod) - Method in class com.bayesserver.CLGaussian
Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
MultipleIterator - Class in com.bayesserver
Provides methods to iterate over multiple distributions.
MultipleIterator.Combination - Interface in com.bayesserver
 
multiply(CLGaussian) - Method in class com.bayesserver.CLGaussian
Multiplies this instance by another CLGaussian distribution.
multiply(Distribution) - Method in class com.bayesserver.CLGaussian
Multiplies this instance by another distribution.
multiply(Distribution) - Method in interface com.bayesserver.Distribution
Creates a new distribution which is the result of multiplying this instance by another distribution.
multiply(Distribution) - Method in class com.bayesserver.Table
Creates a new distribution by multiplying this instance by another distribution.
multiplyInPlace(double) - Method in class com.bayesserver.Table
Multiplies all values in the distribution by the specified value.
multiplyInPlace(Table) - Method in class com.bayesserver.Table
Multiplies the [subset] into this instance.
multiplyInPlace(Table, boolean) - Method in class com.bayesserver.Table
Multiplies the [subset] into this instance.
MutualInformation - Class in com.bayesserver.statistics
Calculates mutual information or conditional mutual information, which measures the dependence between two variables.

N

NameValuesReader - Interface in com.bayesserver
Interface for reading name/value pairs.
NameValuesWriter - Interface in com.bayesserver
Interface for writing name/value pairs.
NestedDataReader - Class in com.bayesserver.data
Allows nested table to be read using a DefaultDataReader.
NestedDataReader(DataReader, String) - Constructor for class com.bayesserver.data.NestedDataReader
Initializes a new instance of the NestedDataReader class.
NestedReadInfo - Class in com.bayesserver.data
Provides information about a nested table record.
NestedReadInfo(DataRecord) - Constructor for class com.bayesserver.data.NestedReadInfo
Initializes a new instance of the NestedReadInfo class.
Network - Class in com.bayesserver
Represents a Bayesian Network, or a Dynamic Bayesian Network.
Network() - Constructor for class com.bayesserver.Network
Initializes a new instance of the Network class.
Network(String) - Constructor for class com.bayesserver.Network
Initializes a new instance of the Network class with the specified [name].
NetworkLinkCollection - Class in com.bayesserver
Represents the collection of directed links maintained by the Network class.
NetworkMonitor - Interface in com.bayesserver
For internal use.
NetworkNodeCollection - Class in com.bayesserver
Represents the collection of Network.getNodes() maintained by the Network class.
NetworkNodeGroupCollection - Class in com.bayesserver
A collection of groups.
NetworkVariableCollection - Class in com.bayesserver
Represents a read-only collection of variables that belong to a network.
newDistribution(int) - Method in class com.bayesserver.Node
Creates a new distribution suitable for the requested temporal order, however it is not assigned to the node.
newDistribution(NodeDistributionKey) - Method in class com.bayesserver.Node
Creates a new distribution suitable for the requested temporal order/related node, however it is not assigned to the node.
newDistribution(NodeDistributionKind) - Method in class com.bayesserver.Node
Creates a new distribution with the given kind, however it is not assigned to the node.
newDistribution(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.Node
Creates a new distribution suitable for the requested temporal order/related node, however it is not assigned to the node.
newDistribution() - Method in class com.bayesserver.Node
Creates a new distribution suitable for the node, however does not assign it to the node's Node.getDistribution() property.
newRow() - Method in class com.bayesserver.data.DataTable
Creates a new row of data, but does not add it to the table.
Node - Class in com.bayesserver
Represents a node with one or more variables in a Bayesian network.
Node() - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class, with no variables, and no name.
Node(Variable) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class with a specified Variable and assigns the name of the variable to the node.
Node(String, VariableValueType, VariableKind) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class with the specified [name].
Node(String, VariableValueType) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class with the specified [name].
Node(String, int) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class with the specified [name] and automatically adds a discrete Variable with the number of states specified in [states].
Node(String, String[]) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class, with the name of the node, automatically creating an associated discrete Variable and adds the states specified in [states] to that variable.
Node(String, State...) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class, with the name of the node, automatically creating an associated discrete Variable and adds the states specified in [states] to that variable.
Node(String, Variable...) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class with a specified name and a number of variables.
Node(String, List<Variable>) - Constructor for class com.bayesserver.Node
Initializes a new instance of the Node class with a specified name and a number of variables.
nodeCollectionChange(int, Node, Node, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor
For internal use.
NodeDistributionKey - Class in com.bayesserver
Identifies a distribution assigned or to be assigned to a node.
NodeDistributionKey() - Constructor for class com.bayesserver.NodeDistributionKey
Initializes a new instance of the NodeDistributionKey class with defaults.
NodeDistributionKey(int) - Constructor for class com.bayesserver.NodeDistributionKey
Initializes a new instance of a NodeDistributionKey.
NodeDistributionKey(Node) - Constructor for class com.bayesserver.NodeDistributionKey
Initializes a new instance of a NodeDistributionKey.
NodeDistributionKey(int, Node) - Constructor for class com.bayesserver.NodeDistributionKey
Initializes a new instance of a NodeDistributionKey.
NodeDistributionKind - Enum in com.bayesserver
The kind of distribution, such as a standard Probability or Experience table.
NodeDistributionOptions - Class in com.bayesserver
Options that apply to all distributions of a particular node.
NodeDistributions - Class in com.bayesserver
Represents the distributions assigned to a Node.
NodeDistributions.DistributionOrder - Class in com.bayesserver
Identifies a distribution and its temporal order.
NodeGroup - Class in com.bayesserver
Allows nodes to be assigned to one or more groups.
NodeGroup(String) - Constructor for class com.bayesserver.NodeGroup
Initializes a new instance of the NodeGroup class.
NodeGroupCollection - Class in com.bayesserver
Represents the collection of groups a node belongs to.
NodeLinkCollection - Class in com.bayesserver
Represents a read-only collection of links.
NodeVariableCollection - Class in com.bayesserver
Represents the collection of variables belonging to a
noisyNodeTypeChanged(Node, NoisyType, NoisyType) - Method in interface com.bayesserver.NetworkMonitor
For internal use.
NoisyOrder - Enum in com.bayesserver
Determines the order in which the states of a parent of a noisy node increasingly affect the noisy states.
noisyOrderChanged(Link, NoisyOrder, NoisyOrder) - Method in interface com.bayesserver.NetworkMonitor
For internal use.
NoisyType - Enum in com.bayesserver
Identifies the noisy node type, if any.
nonZero(Table.NonZeroValues) - Method in class com.bayesserver.Table
Returns any non zero table values, keyed by index.
normalize(boolean) - Method in class com.bayesserver.Table
Normalizes the distribution such that each parent combination sums to 1.
normalize() - Method in class com.bayesserver.Table
Normalizes the distribution such that each parent combination sums to 1.
NotInDomainException - Exception in com.bayesserver
Raised when the arguments to a mathematic function are not in the domain of the function (undefined).
NotInDomainException() - Constructor for exception com.bayesserver.NotInDomainException
Initializes a new instance of the NotInDomainException class.
NotInDomainException(String) - Constructor for exception com.bayesserver.NotInDomainException
Initializes a new instance of the NotInDomainException class with a specified error message.
NotInDomainException(String, Throwable) - Constructor for exception com.bayesserver.NotInDomainException
Initializes a new instance of the NotInDomainException class with a specified error message and a reference to the inner exception that is the cause of this exception.
NotInDomainException(Throwable) - Constructor for exception com.bayesserver.NotInDomainException
Initializes a new instance of the NotInDomainException class with a reference to the inner exception that is the cause of this exception.
NotSpdException - Exception in com.bayesserver
Raised when a matrix is not positive definite.
NotSpdException() - Constructor for exception com.bayesserver.NotSpdException
Initializes a new instance of the NotSpdException class.
NotSpdException(String) - Constructor for exception com.bayesserver.NotSpdException
Initializes a new instance of the NotSpdException class with a specified error message.
NotSpdException(String, Throwable) - Constructor for exception com.bayesserver.NotSpdException
Initializes a new instance of the NotSpdException class with a specified error message and a reference to the inner exception that is the cause of this exception.
NotSpdException(Throwable) - Constructor for exception com.bayesserver.NotSpdException
Initializes a new instance of the NotSpdException class with a reference to the inner exception that is the cause of this exception.
numericValue() - Method in interface com.bayesserver.data.CrossValidationTestResult
Returns the test result as a numeric value if supported, otherwise returns null.
numericValue() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
Returns the test result as a numeric value if supported, otherwise returns null.
numericValue() - Method in class com.bayesserver.data.R2CrossValidationTestResult
Returns the test result as a numeric value if supported, otherwise returns null.

O

oneWay(Evidence, State, ParameterReference) - Method in class com.bayesserver.analysis.SensitivityToParameters
Calculates how a hypothesis varies based on changes to a single parameter.
oneWayDifference(SensitivityFunctionOneWay, SensitivityFunctionOneWay, Interval<Double>) - Static method in class com.bayesserver.analysis.ParameterTuning
Given a pair of sensitivity functions (evaluated on the same parameter and evidence but different hypotheses), determines how the parameter under consideration can be altered so that the difference between the hypothesis probabilities P(h1|e) - P(h2|e) is within a given range.
oneWayRatio(SensitivityFunctionOneWay, SensitivityFunctionOneWay, Interval<Double>) - Static method in class com.bayesserver.analysis.ParameterTuning
Given a pair of sensitivity functions (evaluated on the same parameter and evidence but different hypotheses), determines how the parameter under consideration can be altered so that the ratio between the hypothesis probabilities P(h1|e) / P(h2|e) is within a given range.
oneWaySimple(SensitivityFunctionOneWay, Interval<Double>) - Static method in class com.bayesserver.analysis.ParameterTuning
Given a sensitivity function, determines how the parameter under consideration can be altered so that the resulting value of the hypothesis is within a given range.
OnlineLearning - Class in com.bayesserver.learning.parameters
Adapts the parameters of a Bayesian network, using Bayesian statistics.
OnlineLearning(Network, InferenceFactory) - Constructor for class com.bayesserver.learning.parameters.OnlineLearning
Initializes a new instance of the OnlineLearning class.
OnlineLearningOptions - Class in com.bayesserver.learning.parameters
Options for online learning (adaptation using Bayesian statistics).
OnlineLearningOptions() - Constructor for class com.bayesserver.learning.parameters.OnlineLearningOptions
 

P

ParameterCounter - Class in com.bayesserver
Contains methods to determine the number of parameters in a Bayesian network or distribution.
ParameterCountOptions - Class in com.bayesserver
Options for ParameterCounter.
ParameterCountOptions() - Constructor for class com.bayesserver.ParameterCountOptions
 
ParameterLearning - Class in com.bayesserver.learning.parameters
Learns the parameters of Bayesian networks and Dynamic Bayesian networks, from data.
ParameterLearning(Network, InferenceFactory) - Constructor for class com.bayesserver.learning.parameters.ParameterLearning
Initializes a new instance of the ParameterLearning class.
ParameterLearningOptions - Class in com.bayesserver.learning.parameters
Options governing parameter learning.
ParameterLearningOptions() - Constructor for class com.bayesserver.learning.parameters.ParameterLearningOptions
 
ParameterLearningOutput - Class in com.bayesserver.learning.parameters
ParameterLearningProgress - Interface in com.bayesserver.learning.parameters
Interface to provide progress information during parameter learning.
ParameterLearningProgressInfo - Class in com.bayesserver.learning.parameters
ParameterReference - Class in com.bayesserver.analysis
References a parameter in a node distribution.
ParameterReference(Node, State[]) - Constructor for class com.bayesserver.analysis.ParameterReference
Initializes a new instance of the ParameterReference class.
ParameterReference(Node, NodeDistributionKey, State[]) - Constructor for class com.bayesserver.analysis.ParameterReference
Initializes a new instance of the ParameterReference class .
ParameterTuning - Class in com.bayesserver.analysis
Calculates how a parameter can be updated so that the resulting value of a hypothesis is within a given range.
ParameterTuningOneWay - Class in com.bayesserver.analysis
Represents the result of one way parameter tuning.
PartitionDataReaderFilter - Class in com.bayesserver.data
A data reader filter based on an integer column, which can contain ids or a zero based partition identifier.
PartitionDataReaderFilter(DataPartitioning, int, String) - Constructor for class com.bayesserver.data.PartitionDataReaderFilter
Initializes a new instance of the PartitionDataReaderFilter class.
PCLinkOutput - Class in com.bayesserver.learning.structure
Contains information about a new link learnt using the com.bayesserver.learning.structure.pc.PCStructuralLearning algorithm.
PCStructuralLearning - Class in com.bayesserver.learning.structure
A structural learning algorithm for Bayesian networks based on the PC algorithm.
PCStructuralLearning() - Constructor for class com.bayesserver.learning.structure.PCStructuralLearning
 
PCStructuralLearningOptions - Class in com.bayesserver.learning.structure
Options for structural learning with the com.bayesserver.learning.structure.pc.PCStructuralLearning class.
PCStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.PCStructuralLearningOptions
Initializes a new instance of the com.bayesserver.learning.structure.pc.PCStructuralLearningOptions class.
PCStructuralLearningOutput - Class in com.bayesserver.learning.structure
Contains information returned from the com.bayesserver.learning.structure.pc.PCStructuralLearning algorithm.
PCStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
Progress information returned from the PC structural learning algorithm.
Priors - Class in com.bayesserver.learning.parameters
Contains parameters used to avoid boundary conditions during learning.
PropagationMethod - Enum in com.bayesserver
The propagation method used during inference.

Q

query(QueryOptions, QueryOutput) - Method in interface com.bayesserver.inference.Inference
Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.LoopyBeliefInference
Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.RelevanceTreeInference
Calculates a number of distributions, e.g.
query(Network, QueryDistributionCollection, Evidence, TreeQueryOptions) - Static method in class com.bayesserver.inference.TreeQuery
Calculates properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.VariableEliminationInference
Calculates a number of distributions, e.g.
QueryComparison - Enum in com.bayesserver.inference
Determines whether and how queried values (e.g.
QueryDistance - Enum in com.bayesserver.inference
Type of distance to calculate for a query.
QueryDistribution - Class in com.bayesserver.inference
QueryDistribution(Distribution) - Constructor for class com.bayesserver.inference.QueryDistribution
Initializes a new instance of the QueryDistribution class.
QueryDistribution(Distribution, boolean) - Constructor for class com.bayesserver.inference.QueryDistribution
Initializes a new instance of the QueryDistribution class.
QueryDistributionCollection - Interface in com.bayesserver.inference
QueryEvidenceMode - Enum in com.bayesserver.inference
Determines how predictions on variables with evidence are performed.
QueryOptions - Interface in com.bayesserver.inference
QueryOutput - Interface in com.bayesserver.inference
Returns any information, in addition to the distributions, that is requested from a query.
QuerySamplingOptions - Interface in com.bayesserver.inference
Interface for approximate sampling inference algorithms, which can be implemented in addition to QueryOptions.

R

R2CrossValidationTestResult - Class in com.bayesserver.data
Represents the R Squared statistic (Coefficient of determination) on a partition of data.
R2CrossValidationTestResult(double, double, double, double) - Constructor for class com.bayesserver.data.R2CrossValidationTestResult
Initializes a new instance of the R2CrossValidationTestResult class.
randomize(Random) - Method in class com.bayesserver.Table
Randomizes the distribution such that each parent combination sums to 1.
read() - Method in interface com.bayesserver.data.DataReader
Moves to the next record, if any exist.
read() - Method in class com.bayesserver.data.DataReaderFiltered
Moves to the next record, if any exist.
read() - Method in class com.bayesserver.data.DataTableReader
 
read() - Method in class com.bayesserver.data.DefaultDataReader
Reads the next (non temporal) record.
read(Evidence, ReadOptions) - Method in class com.bayesserver.data.DefaultEvidenceReader
Reads the next case (record).
read(Evidence, ReadOptions) - Method in interface com.bayesserver.data.EvidenceReader
Reads the next case (record).
read() - Method in class com.bayesserver.data.timeseries.WindowDataReader
Moves to the next record, if any exist.
read(String) - Method in interface com.bayesserver.NameValuesReader
Reads the value (as a stream) for a particular name.
ReaderOptions - Class in com.bayesserver.data
Options that apply to the reading of non temporal data.
ReaderOptions() - Constructor for class com.bayesserver.data.ReaderOptions
Initializes a new instance of the ReaderOptions class.
ReaderOptions(String) - Constructor for class com.bayesserver.data.ReaderOptions
Initializes a new instance of the ReaderOptions class.
ReaderOptions(String, String) - Constructor for class com.bayesserver.data.ReaderOptions
Initializes a new instance of the ReaderOptions class.
ReadInfo - Class in com.bayesserver.data
Provides information about a non temporal record.
ReadInfo(Object, DataRecord) - Constructor for class com.bayesserver.data.ReadInfo
Initializes a new instance of the ReadInfo struct.
ReadInfo(Object, double, DataRecord) - Constructor for class com.bayesserver.data.ReadInfo
Initializes a new instance of the ReadInfo class.
readNested(int) - Method in class com.bayesserver.data.DefaultDataReader
Reads the next record from a nested table.
ReadOptions - Interface in com.bayesserver.data
readTemporal() - Method in class com.bayesserver.data.DefaultDataReader
Reads the next temporal record.
readTemporal(Evidence, ReadOptions) - Method in class com.bayesserver.data.DefaultEvidenceReader
Reads the next temporal record, setting evidence.
RegressionStatistics - Class in com.bayesserver.analysis
Calculates statistics for a network which is used to predict continuous values (regression).
RelevanceTreeInference - Class in com.bayesserver.inference
An exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.
RelevanceTreeInference(Network) - Constructor for class com.bayesserver.inference.RelevanceTreeInference
Initializes a new instance of the RelevanceTreeInference class, with the target Bayesian network.
RelevanceTreeInferenceFactory - Class in com.bayesserver.inference
Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
RelevanceTreeInferenceFactory() - Constructor for class com.bayesserver.inference.RelevanceTreeInferenceFactory
 
RelevanceTreeQueryOptions - Class in com.bayesserver.inference
RelevanceTreeQueryOptions() - Constructor for class com.bayesserver.inference.RelevanceTreeQueryOptions
Initializes a new instance of the RelevanceTreeQueryOptions class.
RelevanceTreeQueryOutput - Class in com.bayesserver.inference
Returns any information, in addition to the distributions, that is requested from a query.
RelevanceTreeQueryOutput() - Constructor for class com.bayesserver.inference.RelevanceTreeQueryOutput
Initializes a new instance of the RelevanceTreeQueryOutput class.
remove(int) - Method in class com.bayesserver.CustomPropertyCollection
remove(int) - Method in class com.bayesserver.data.DataColumnCollection
Removes the DataColumn at the given index.
remove(int) - Method in class com.bayesserver.data.DataRowCollection
Removes the row at the given index.
remove(int) - Method in class com.bayesserver.data.sampling.ExcludedVariables
remove(int) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
remove(int) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
 
remove(Link) - Method in class com.bayesserver.NetworkLinkCollection
Removes the Link from the collection.
remove(int) - Method in class com.bayesserver.NetworkLinkCollection
Removes an element from the collection at the specified index.
remove(Node) - Method in class com.bayesserver.NetworkNodeCollection
Removes the Node from the collection.
remove(int) - Method in class com.bayesserver.NetworkNodeCollection
Removes an element from the collection at the specified index, and any links that it has.
remove(int) - Method in class com.bayesserver.NetworkNodeGroupCollection
remove(String) - Method in class com.bayesserver.NodeGroupCollection
Removes the group from the collection.
remove(int) - Method in class com.bayesserver.NodeGroupCollection
Removes an element from the collection at the specified index.
remove(Variable) - Method in class com.bayesserver.NodeVariableCollection
Removes the Variable from the collection.
remove(int) - Method in class com.bayesserver.NodeVariableCollection
Removes an element from the collection at the specified index.
remove(int) - Method in class com.bayesserver.StateCollection
Removes an element from the collection at the specified index.
removeMonitor(NetworkMonitor) - Method in class com.bayesserver.Network
For internal use.
reset() - Method in class com.bayesserver.CLGaussian
Resets all mean, covariance and weight entries to zero.
reset() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Resets all values to their defaults.
reset() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
Resets all values to their defaults.
reset() - Method in interface com.bayesserver.inference.QueryOutput
Resets all values to their defaults.
reset() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
Resets all values to their defaults.
reset() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
Resets all values to their defaults.
reset() - Method in class com.bayesserver.TableIterator
Resets the iterator to the start.
reverse(Link) - Static method in class com.bayesserver.ArcReversal
Reverse the direction of a Link (known as arc reversal).

S

save(OutputStream) - Method in class com.bayesserver.inference.DefaultEvidence
Saves the evidence to the specified stream.
save(String) - Method in class com.bayesserver.inference.DefaultEvidence
Saves the specified to the specified file.
save(OutputStream) - Method in interface com.bayesserver.inference.Evidence
Saves the evidence to the specified stream.
save(String) - Method in interface com.bayesserver.inference.Evidence
Saves the specified to the specified file.
save(OutputStream) - Method in class com.bayesserver.Network
Saves this Network to the specified output OutputStream.
save(String) - Method in class com.bayesserver.Network
Saves this Network to the specified [path] overwriting the file if it already exists.
saveToString(String) - Method in class com.bayesserver.Network
Saves the network to a string, with the specified encoding.
saveToString() - Method in class com.bayesserver.Network
Saves the network to a string, with UTF-8 encoding.
ScoreMethod - Enum in com.bayesserver.learning.structure
The scoring mechanism used to evaluate different Bayesian network structures during a search.
SearchLinkOutput - Class in com.bayesserver.learning.structure
Contains information about a new link learnt using the com.bayesserver.learning.structure.search.SearchStructuralLearning algorithm.
SearchStructuralLearning - Class in com.bayesserver.learning.structure
A structural learning algorithm for Bayesian networks based on Search and Score.
SearchStructuralLearning() - Constructor for class com.bayesserver.learning.structure.SearchStructuralLearning
 
SearchStructuralLearningOptions - Class in com.bayesserver.learning.structure
Options for structural learning with the com.bayesserver.learning.structure.search.SearchStructuralLearning class.
SearchStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.SearchStructuralLearningOptions
 
SearchStructuralLearningOutput - Class in com.bayesserver.learning.structure
Contains information returned from the com.bayesserver.learning.structure.search.SearchStructuralLearning algorithm.
SearchStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
Progress information returned from the Search based structural learning algorithm.
SensitivityFunctionOneWay - Class in com.bayesserver.analysis
Represents the result on a one-way sensitivity to parameters analysis.
SensitivityFunctionTwoWay - Class in com.bayesserver.analysis
Represents the result on a two-way sensitivity to parameters analysis.
SensitivityToParameters - Class in com.bayesserver.analysis
Calculates the affect of one or more parameters on the value of a hypothesis.
SensitivityToParameters(Network, InferenceFactory) - Constructor for class com.bayesserver.analysis.SensitivityToParameters
Initializes a new instance of the SensitivityToParameters class .
set(int, CustomProperty) - Method in class com.bayesserver.CustomPropertyCollection
 
set(int, Object) - Method in class com.bayesserver.data.DataRow
Sets the value at the specified index.
set(int, Variable) - Method in class com.bayesserver.data.sampling.ExcludedVariables
set(Variable, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
Sets temporal evidence on a variable.
set(Node, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
Sets temporal evidence on a node with a single variable.
set(Variable, Double) - Method in class com.bayesserver.inference.DefaultEvidence
Sets a variable to a particular value (hard evidence).
set(Node, Double, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets evidence on a node's single variable at a specified time.
set(Variable, Double, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets evidence on a variable at a specified time.
set(Node, Double) - Method in class com.bayesserver.inference.DefaultEvidence
Sets a node's variable to a particular value (hard evidence).
set(int, QueryDistribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
set(Variable, Double) - Method in interface com.bayesserver.inference.Evidence
Sets a variable to a particular value (hard evidence).
set(Variable, Double, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets evidence on a variable at a specified time.
set(Node, Double, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets evidence on a node's single variable at a specified time.
set(Variable, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
Sets temporal evidence on a variable.
set(Node, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
Sets temporal evidence on a node with a single variable.
set(Node, Double) - Method in interface com.bayesserver.inference.Evidence
Sets a node's variable to a particular value (hard evidence).
set(int, LinkConstraint) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
 
set(int, Link) - Method in class com.bayesserver.NetworkLinkCollection
Sets the Link object at the specified index.
set(int, Node) - Method in class com.bayesserver.NetworkNodeCollection
Sets the Node object at the specified index.
set(int, NodeGroup) - Method in class com.bayesserver.NetworkNodeGroupCollection
 
set(int, Variable) - Method in class com.bayesserver.NetworkVariableCollection
Gets the Variable object at the specified index.
set(int, Distribution) - Method in class com.bayesserver.NodeDistributions
Sets a distribution at a particular temporal order.
set(NodeDistributionKey, Distribution) - Method in class com.bayesserver.NodeDistributions
Sets a distribution with particular properties, such as temporal order.
set(NodeDistributionKind, Distribution) - Method in class com.bayesserver.NodeDistributions
Sets a particular kind of distribution on the node.
set(NodeDistributionKey, NodeDistributionKind, Distribution) - Method in class com.bayesserver.NodeDistributions
Sets a distribution with particular properties, such as temporal order.
set(int, String) - Method in class com.bayesserver.NodeGroupCollection
Sets the group at the specified index.
set(int, Variable) - Method in class com.bayesserver.NodeVariableCollection
Sets the Variable object at the specified index.
set(int, State) - Method in class com.bayesserver.StateCollection
Sets the State at the specified index.
set(double, State...) - Method in class com.bayesserver.Table
Sets the table value corresponding to the given states.
set(double, StateContext...) - Method in class com.bayesserver.Table
Sets the table value corresponding to the given states and associated times.
set(int, double) - Method in class com.bayesserver.Table
Sets the Table value at the specified index into the 1-dimensional array.
set(int[], double) - Method in class com.bayesserver.TableAccessor
Sets the underlying Table value, using states corresponding to the order of variables in the TableAccessor.
set(int, double) - Method in class com.bayesserver.TableAccessor
Sets the underlying Table value, specified i.
set(int, VariableContext) - Method in class com.bayesserver.VariableContextCollection
Gets the Variable object at the specified index.
setAddNodeGroups(boolean) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets a value which determines whether network node groups are added for each group in a level.
setAll(double) - Method in class com.bayesserver.Table
Sets all values in the Table to a specified value.
setAllowNullDistributions(boolean) - Method in class com.bayesserver.ValidationOptions
Determines whether validation should succeed even if the required distribution(s) have not been assigned to a node.
setAutoCommit(boolean) - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Sets the auto commit value to be set on each connection created.
setAutoDetectDiscreteLimit(int) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Sets the distinct value count, which when exceeded changes a variable from discrete to continuous.
setAutoReadTemporal(boolean) - Method in class com.bayesserver.data.DefaultEvidenceReader
Determines whether any temporal data is read automatically.
setBaseEvidence(Evidence) - Method in interface com.bayesserver.inference.Inference
Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.LoopyBeliefInference
Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.RelevanceTreeInference
Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.VariableEliminationInference
Optional evidence which can be used to calculate the lift of queries.
setBounds(Bounds) - Method in class com.bayesserver.Node
Sets the size and location of the node.
setCalculateStatistics(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets a value indicating whether to calculate summary statistics in an extra iteration at the end of learning.
setCancel(boolean) - Method in interface com.bayesserver.Cancellation
When set to true attempts to cancel a long running operation.
setCancel(boolean) - Method in class com.bayesserver.DefaultCancellation
When set to true attempts to cancel a long running operation.
setCancellation(Cancellation) - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
Gets of sets an instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets of sets an instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Allows cancellation of a query.
setCancellation(Cancellation) - Method in interface com.bayesserver.inference.QueryOptions
Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.TreeQueryOptions
Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
Used to cancel a long running operation.
setCleared(boolean) - Method in class com.bayesserver.data.DefaultReadOptions
Sets a value indicating whether the Evidence has been cleared prior to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions) being called.
setColumnName(String) - Method in class com.bayesserver.data.discovery.DiscretizationColumn
Sets the name of the column of data to be discretized.
setComparison(QueryComparison) - Method in class com.bayesserver.inference.QueryDistribution
Sets a value indicating whether queried values should be adjusted to show how they compare to the same query with no evidence, or base evidence.
setConflict(boolean) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(Double) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Sets the conflict measure.
setConflict(boolean) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(Double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
Sets the conflict measure.
setConflict(boolean) - Method in interface com.bayesserver.inference.QueryOptions
Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(Double) - Method in interface com.bayesserver.inference.QueryOutput
Sets the conflict measure.
setConflict(boolean) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(Double) - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
Sets the conflict measure.
setConflict(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(Double) - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
Sets the conflict measure.
setContinuous(double) - Method in class com.bayesserver.learning.parameters.Priors
Sets the amount continuous distributions are adjusted during learning.
setContinuousTargetInterval(Interval<Double>) - Method in class com.bayesserver.analysis.AutoInsightOutput
Gets the target interval (if any).
setConvergenceMethod(ConvergenceMethod) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the method used to determine convergence of the learning algorithm.
setCovariance(int, int, int, double) - Method in class com.bayesserver.CLGaussian
Sets the covariance value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setCovariance(Variable, Variable, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(VariableContext, VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
setCovariance(Variable, Variable, double) - Method in class com.bayesserver.CLGaussian
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
setCovariance(VariableContext, VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(VariableContext, VariableContext, double) - Method in class com.bayesserver.CLGaussian
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
setCovariance(Variable, Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(VariableContext, VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setDataColumn(String) - Method in class com.bayesserver.data.discovery.VariableDefinition
The name of the data column, containing the data used to generate the new variable.
setDataProgress(DataProgress) - Method in class com.bayesserver.data.DefaultEvidenceReader
Gets the instance used to report progress on the number of cases read.
setDataProgress(DataProgress) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Reports progress on the number of cases read.
setDataProgressInterval(int) - Method in class com.bayesserver.data.DefaultEvidenceReader
Sets a value which determines how often progress events are raised.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in interface com.bayesserver.inference.QueryOptions
Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.learning.parameters.OnlineLearningOptions
Sets the algorithm to use for adaption of decision graphs.
setDecisionPostProcessing(DecisionPostProcessingMethod) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the post processing method for decision nodes.
setDescription(String) - Method in class com.bayesserver.CustomProperty
An optional description for the custom property.
setDescription(String) - Method in class com.bayesserver.Link
Optional description for the link.
setDescription(String) - Method in class com.bayesserver.Network
An optional description for the Bayesian network.
setDescription(String) - Method in class com.bayesserver.Node
An optional description for the node.
setDescription(String) - Method in class com.bayesserver.NodeGroup
An optional description for the custom property.
setDescription(String) - Method in class com.bayesserver.State
Sets an optional description for the state.
setDescription(String) - Method in class com.bayesserver.Variable
An optional description for the variable.
setDetectIntegralFloats(boolean) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Sets a value, which when true tests floating point column data to see if the data is an integral type, which would then become a candidate to be a discrete variable when VariableValueType is not specified.
setDiscrete(double) - Method in class com.bayesserver.learning.parameters.Priors
Sets the amount distributions containing discrete variables are adjusted during learning.
setDiscretePriorMethod(DiscretePriorMethod) - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Sets the type of discrete prior to use for this distribution.
setDiscretePriorMethod(DiscretePriorMethod) - Method in class com.bayesserver.learning.parameters.Priors
The default discrete prior to use for discrete distributions during parameter learning.
setDiscretizationMethod(DiscretizationMethod) - Method in class com.bayesserver.data.discovery.VariableDefinition
Sets the method (algorithm) to use for discretization, if any.
setDistance(Double) - Method in class com.bayesserver.inference.QueryDistribution
The distance between this query calculated with base evidence or no evidence, and when calculated with evidence.
setDistribution(Distribution) - Method in class com.bayesserver.Node
Returns the distribution currently associated with the Node.
setEmptyStringAction(EmptyStringAction) - Method in class com.bayesserver.data.discovery.VariableDefinition
Determines the action to take if an empty string is encountered.
setEnsureTestWithoutCluster(boolean) - Method in class com.bayesserver.analysis.ClusterCountOptions
Sets a value which indicates whether a test must be included which excludes the cluster variable altogether.
setEnumerateAllMissing(boolean) - Method in class com.bayesserver.analysis.CombinationOptions
Sets a value which indicates whether the combination where all states are null/missing should be included in the enumeration.
setEnumerateMissing(boolean) - Method in class com.bayesserver.analysis.CombinationOptions
Sets a value which indicates whether null/missing values should be enumerated in addition to each state.
setEvidence(Evidence) - Method in interface com.bayesserver.inference.Inference
Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.LoopyBeliefInference
Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.RelevanceTreeInference
Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.VariableEliminationInference
Sets the evidence (case data, e.g.
setExcludeNullDistributions(boolean) - Method in class com.bayesserver.ParameterCountOptions
Sets a value indicating whether null distributions are excluded from the parameter count.
setFactory(InferenceFactory) - Method in class com.bayesserver.analysis.ImpactOptions
Sets the inference factory which is used to create inference engines during an impact analysis.
setFactory(InferenceFactory) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Sets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
setFetchSize(int) - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Sets the fetch size to be set on each statement created.
setFixedData(Evidence) - Method in class com.bayesserver.data.sampling.DataSampler
Sets any evidence that should be fixed for each sample.
setGap(double) - Method in class com.bayesserver.DecomposeOptions
The gap between decomposed nodes, used when laying out new nodes.
setHasZeroIntercepts(boolean) - Method in class com.bayesserver.NodeDistributionOptions
Determines whether CLGaussian intercept terms are fixed to zero.
setIncludeGlobalCovariance(boolean) - Method in class com.bayesserver.learning.parameters.Priors
When Gaussian distributions are adjusted according to the Priors.getContinuous() prior, this property determines whether the global covariance should be included in the adjustment, as well as the global variance.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.AssociationOptions
Sets the inference factory used for link strength calculations.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.AutoInsightOptions
Sets the inference factory used for link strength calculations.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.ClusterCountOptions
Sets the factory which is used to create inference engines during the cluster count tests.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
Sets the factory which is used to create inference engines during the in-sample anomaly detection process.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Sets the inference factory used during scoring.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets the inference factory used during scoring.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets the inference factory used during scoring.
setInfiniteExtremes(boolean) - Method in class com.bayesserver.data.discovery.DiscretizationOptions
Sets a value indicating whether the first and last intervals extend to negative and positive infinity respectively.
setInitialize(Boolean) - Method in class com.bayesserver.learning.parameters.DistributionSpecification
Sets a flag indicating whether the distribution should be initialized.
setInitializeDistributions(boolean) - Method in class com.bayesserver.learning.parameters.InitializationOptions
Indicates whether or not to initialize distributions by default.
setIsApproximate(boolean) - Method in class com.bayesserver.analysis.AutoInsightOutput
Gets a value which when true indicates that the auto-insight calculations were approximated using sampling.
setIsEnabled(boolean) - Method in class com.bayesserver.inference.QueryDistribution
Sets a value indicating whether the distribution should be queried.
setIsImpliedEvidenceEnabled(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets a value indicating whether to detect implied evidence during the calculation.
setIsInternal(boolean) - Method in class com.bayesserver.Network
For internal use only.
setJSDivergence(AutoInsightJSDivergence) - Method in class com.bayesserver.analysis.AutoInsightOptions
Sets a value which determines the type of Jensen Shannon divergence calculations to perform, if any.
setKeepEvidenceNotAnalyzed(boolean) - Method in class com.bayesserver.analysis.ImpactOptions
Sets a value which when true retains evidence not being analysed, or when false ignores it.
setKeepEvidenceNotAnalyzed(boolean) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Sets a value which when true retains evidence not being analysed, or when false ignores it.
setKind(VariableKind) - Method in class com.bayesserver.data.discovery.VariableDefinition
Sets the VariableKind for the new variable.
setKLDivergence(AutoInsightKLDivergence) - Method in class com.bayesserver.analysis.AutoInsightOptions
Sets a value which determines the type of KL divergence calculations to perform, if any.
setLocked(boolean) - Method in class com.bayesserver.CLGaussian
Locks or unlocks a distribution.
setLocked(boolean) - Method in interface com.bayesserver.Distribution
Locks or unlocks a distribution.
setLocked(boolean) - Method in class com.bayesserver.Table
Locks or unlocks a distribution.
setLogarithmBase(LogarithmBase) - Method in class com.bayesserver.analysis.ValueOfInformationOptions
The logarithm base to use when calculating ValueOfInformation.
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(Double) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Sets the log-likelihood value.
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(Double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
Sets the log-likelihood value.
setLogLikelihood(Double) - Method in class com.bayesserver.inference.QueryDistribution
The log-likelihood specific to the evidence used to calculate this query.
setLogLikelihood(boolean) - Method in interface com.bayesserver.inference.QueryOptions
Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(Double) - Method in interface com.bayesserver.inference.QueryOutput
Sets the log-likelihood value.
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(Double) - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
Sets the log-likelihood value.
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(Double) - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
Sets the log-likelihood value.
setLogWeight(double) - Method in class com.bayesserver.inference.DefaultEvidence
Sets the natural logarithm of Evidence.getWeight().
setLogWeight(double) - Method in interface com.bayesserver.inference.Evidence
Sets the natural logarithm of Evidence.getWeight().
setMaxEvidenceSubsetSize(int) - Method in class com.bayesserver.analysis.ImpactOptions
Sets the maximum size of evidence subsets to consider.
setMaxEvidenceSubsetSize(int) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Sets the maximum size of evidence subsets to consider.
setMaximum(T) - Method in class com.bayesserver.Interval
Sets the maximum interval value.
setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
setMaximumClusterCount(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Sets the maximum number of clusters generated.
setMaximumClustersPerGroup(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets the maximum number of clusters generated for each group.
setMaximumConcurrency(Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the maximum number of inference engines used during learning.
setMaximumConditional(int) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Sets the maximum number of conditional variables to consider during independence testing.
setMaximumEndPoint(IntervalEndPoint) - Method in class com.bayesserver.Interval
Sets the end point type for the maximum value of the interval.
setMaximumGroupsPerLevel(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets the maximum number of groups created per level.
setMaximumIterations(int) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the maximum number of iterations that parameter learning will perform.
setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Sets the maximum number of iterations used by parameter learning to score each configuration.
setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets the maximum number of iterations used by parameter learning to score each configuration.
setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets the optional maximum number of iterations (moves) made during the search procedure.
setMaximumLevels(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets the maximum number of levels generated by the algorithm.
setMaximumSupport(int) - Method in class com.bayesserver.learning.parameters.InitializationOptions
Limits the amount of support each distribution is given during initialization.
setMaximumTemporalOrder(int) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Sets the maximum order of temporal links that are considered during learning.
setMean(int, int, double) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setMean(Variable, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, double) - Method in class com.bayesserver.CLGaussian
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setMean(Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setMean(Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
setMean(Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
setMean(VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
setMethod(InitializationMethod) - Method in class com.bayesserver.learning.parameters.InitializationOptions
Determines the algorithm used for initialization.
setMinimum(T) - Method in class com.bayesserver.Interval
Sets the minimum interval value.
setMinimumEndPoint(IntervalEndPoint) - Method in class com.bayesserver.Interval
Sets the end point type for the minimum value of the interval.
setMissingDataProbability(double) - Method in class com.bayesserver.data.sampling.DataSamplingOptions
When positive, sets a certain percentage of values to missing (except when DataSamplingOptions.getMissingDataProbabilityMin() has a value).
setMissingDataProbabilityMin(Double) - Method in class com.bayesserver.data.sampling.DataSamplingOptions
When set, the missing data probability for each case varies randomly between DataSamplingOptions.getMissingDataProbabilityMin() and DataSamplingOptions.getMissingDataProbability().
setMonitorLogLikelihood(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Calculates the log likelihood at each iteration.
setMutualInformation(boolean) - Method in class com.bayesserver.learning.structure.FeatureSelectionOptions
Sets a value which when true calculates the mutual information between each target and test.
setName(String) - Method in class com.bayesserver.data.discovery.VariableDefinition
Sets the name for the new variable.
setName(String) - Method in class com.bayesserver.Network
An optional name for the Bayesian network.
setName(String) - Method in class com.bayesserver.Node
The name of the node.
setName(String) - Method in class com.bayesserver.NodeGroup
Gets the name, which must be unique per NetworkNodeGroupCollection.
setName(String) - Method in class com.bayesserver.State
Sets the name of the state.
setName(String) - Method in class com.bayesserver.Variable
Sets the name of the variable.
setNetwork(Network) - Method in class com.bayesserver.data.DefaultCrossValidationNetwork
Sets the network learnt from a cross validation partitioning.
setNodeWidthOverride(Double) - Method in class com.bayesserver.DecomposeOptions
Sets a value that can be used to override the width of nodes, used when laying out new nodes.
setNodeWidthOverride(Double) - Method in class com.bayesserver.UnrollOptions
Sets a value that can be used to override the width of nodes, used when laying out nodes.
setNoisyOrder(NoisyOrder) - Method in class com.bayesserver.Link
Sets a value which determines the nature of the causal effect between the parent node (from) and a noisy child node (to).
setNoisyType(NoisyType) - Method in class com.bayesserver.NodeDistributionOptions
Sets a value which identifies this node as a noisy node or not.
setOnExecuteReader(ExecuteEvidenceReader) - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand
Sets a function that is called when a new reader is created.
setPartitionCount(int) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets the number of partitions used by scoring functions that use cross validation.
setPartitions(int) - Method in class com.bayesserver.analysis.ClusterCountOptions
Sets the number of cross validation partitions to use.
setPartitions(int) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
Sets the number of cross validation partitions to use.
setPartitions(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Sets the number of cross validation partitions to use when scoring each cluster count.
setPartitions(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Sets the number of cross validation partitions to use when scoring each cluster count.
setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.Clustering
Gets an instance that receive progress notifications.
setProgress(DiscretizeProgress) - Method in interface com.bayesserver.data.discovery.Discretize
Gets an instance that receive progress notifications.
setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.EqualFrequencies
Gets an instance that receive progress notifications.
setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.EqualIntervals
Gets an instance that receive progress notifications.
setProgress(VariableGeneratorProgress) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Gets of sets the instance implementing VariableGeneratorProgress, used for progress notifications.
setProgress(ParameterLearningProgress) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Gets of sets the instance implementing ParameterLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in interface com.bayesserver.inference.QueryOptions
Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
Sets the propagation method to use during marginalization.
setQueryDistance(QueryDistance) - Method in class com.bayesserver.inference.QueryDistribution
Sets a value indicating whether the distance should be calculated between the query calculated with base evidence (or no evidence), and the same query calculated with evidence.
setQueryDistributions(QueryDistributionCollection) - Method in interface com.bayesserver.inference.Inference
Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.LoopyBeliefInference
Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.RelevanceTreeInference
Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.VariableEliminationInference
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in interface com.bayesserver.inference.QueryOptions
Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.TreeQueryOptions
Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Determines whether evidence is retracted for each query.
setQueryLogLikelihood(boolean) - Method in class com.bayesserver.inference.QueryDistribution
Determines whether or not to calculate the QueryDistribution.getLogLikelihood() specific to the evidence used to calculate this query.
setQueryTimeout(int) - Method in class com.bayesserver.data.DatabaseDataReaderCommand
Sets the timeout to be used when statements are executed.
setRoot(Node) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
Sets the root of the Chow-Liu tree.
setRoot(Node) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Sets the root of the TAN tree.
setSampleCount(Integer) - Method in class com.bayesserver.analysis.AutoInsightSamplingOptions
The number of samples used to approximate sufficient statistics, when exact inference is not possible.
setSampleCount(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets a value indicating how many samples cases to generate in order to approximate the current query.
setSampleCount(Integer) - Method in interface com.bayesserver.inference.QuerySamplingOptions
Sets a value indicating how many samples cases to generate in order to approximate the current query.
setSamplingProbability(double) - Method in class com.bayesserver.learning.parameters.InitializationOptions
A value between 0 and 1 (inclusive) indicating what probability of cases to use for initialization.
setSaveHyperparameters(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets a value indicating whether hyperparameters (e.g.
setScoreMethod(ScoreMethod) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets the scoring method used to evaluate search moves.
setSeed(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets an optional seed for the random number generator.
setSeed(int) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
Sets the seed used by the random number generator.
setSeed(Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the seed used to generate random numbers for initialization.
setSequenceLength(Integer) - Method in class com.bayesserver.data.sampling.DataSamplingOptions
The sequence length generated for each sample from networks with temporal nodes.
setShift(int) - Method in class com.bayesserver.data.timeseries.WindowOptions
Sets the number of records between successive windows.
setSignificanceLevel(double) - Method in class com.bayesserver.learning.structure.IndependenceOptions
Sets the significance level used to accept or reject (conditional) independence tests.
setSimpleVariance(double) - Method in class com.bayesserver.learning.parameters.Priors
Used to make a fixed adjustment to all covariance matrices during learning, by increasing each diagonal (variance) entry.
setSliceGap(double) - Method in class com.bayesserver.UnrollOptions
Sets the gap between time slices.
setSortOrder(SortOrder) - Method in class com.bayesserver.data.discovery.VariableDefinition
Sets the sort order for states of a new discrete variable.
setState(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets a discrete variable to a particular state (hard evidence).
setState(Variable, Integer, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
setState(State) - Method in class com.bayesserver.inference.DefaultEvidence
Sets evidence on a discrete state (hard evidence).
setState(State, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
setState(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
setState(Node, Integer, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
setState(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets a discrete variable to a particular state (hard evidence).
setState(Variable, Integer, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
setState(State) - Method in interface com.bayesserver.inference.Evidence
Sets evidence on a discrete state (hard evidence).
setState(State, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
setState(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
setState(Node, Integer, Integer) - Method in interface com.bayesserver.inference.Evidence
Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
setStates(Variable, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
Sets soft evidence for a particular discrete variable.
setStates(Node, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
Sets soft evidence for a discrete node with a single variable.
setStates(Node, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets soft evidence for a discrete node with a single variable, at a specified time.
setStates(Variable, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
Sets soft evidence for a particular discrete variable at a specified time.
setStates(Variable, double[]) - Method in interface com.bayesserver.inference.Evidence
Sets soft evidence for a particular discrete variable.
setStates(Node, double[]) - Method in interface com.bayesserver.inference.Evidence
Sets soft evidence for a discrete node with a single variable.
setStates(Variable, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
Sets soft evidence for a particular discrete variable at a specified time.
setStates(Node, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
Sets soft evidence for a discrete node with a single variable, at a specified time.
setStateValueType(StateValueType) - Method in class com.bayesserver.data.discovery.VariableDefinition
Sets the StateValueType for the new variable.
setStateValueType(StateValueType) - Method in class com.bayesserver.Variable
Sets the type of value that states belonging to this variable can represent.
setStop(boolean) - Method in interface com.bayesserver.Stop
When true, indicates to the algorithm to complete early.
setStopping(Stop) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets the instance implementing Stop used for early stopping.
setSubsetMethod(ImpactSubsetMethod) - Method in class com.bayesserver.analysis.ImpactOptions
Sets a value which determines whether evidence subsets are included, excluded or both.
setSubsetMethod(LogLikelihoodAnalysisSubsetMethod) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
Sets a value which determines whether evidence subsets are included, excluded or both.
setSuggestedBinCount(int) - Method in class com.bayesserver.analysis.HistogramDensityOptions
Sets the approximate number of bins to use to represent the approximate density function.
setSuggestedBinCount(int) - Method in class com.bayesserver.data.discovery.DiscretizationOptions
Sets the number of suggested bins to use during discretization.
setSyncNodeVariableName(boolean) - Static method in class com.bayesserver.Network
When true synchronizes Variable names with their containing Node.
setTarget(Node) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
Sets the target of the TAN tree.
setTemporalType(TemporalType) - Method in class com.bayesserver.Node
The TemporalType of the node.
setTerminalTime(Integer) - Method in class com.bayesserver.analysis.DSeparationOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.analysis.ValueOfInformationOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in interface com.bayesserver.inference.QueryOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTestIndependence(boolean) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets a value which when true uses independence tests to reduce the search space.
setTestSingleCluster(boolean) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
Sets a value which determines whether a test is performed for a single cluster (i.e.
setTimes(int[]) - Method in class com.bayesserver.data.timeseries.WindowOptions
Sets the times to include in the window.
setTimeSeriesMode(TimeSeriesMode) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the mode in which time series distributions are learned.
setTolerance(double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
Sets the tolerance used to determine whether or not the approximate inference process has converged.
setTolerance(Double) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
Sets the tolerance used to determine whether or not parameter learning has converged.
setTolerance(Double) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
Sets the tolerance used to determine whether or not a search move is a significant improvement.
setTreeWidth(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
Sets a value indicating whether or not to calculate the tree width.
setUnweightedCaseCount(long) - Method in class com.bayesserver.data.DataProgressEventArgs
Gets the number of cases read so far.
setUnweightedTemporalCount(Long) - Method in class com.bayesserver.data.DataProgressEventArgs
Gets the number of temporal rows read so far for all cases.
setValue(String) - Method in class com.bayesserver.CustomProperty
The custom property value.
setValue(Object) - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
setValue(Double) - Method in class com.bayesserver.data.discovery.WeightedValue
Sets the value, which can be null.
setValue(Object) - Method in class com.bayesserver.State
Sets an optional value for a state, such as an interval for discretized variables.
setValue(double) - Method in class com.bayesserver.TableIterator
Sets the underlying Table value at the current position of the iterator.
setValueType(VariableValueType) - Method in class com.bayesserver.data.discovery.VariableDefinition
Sets the VariableValueType for the new variable.
setVariance(int, int, double) - Method in class com.bayesserver.CLGaussian
Sets the variance value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setVariance(Variable, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, double) - Method in class com.bayesserver.CLGaussian
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setVariance(Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setVariance(Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setWeight(int, int, int, double) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setWeight(Variable, Variable, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Integer, Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(VariableContext, VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Variable, double) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
setWeight(Variable, Integer, Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Integer, Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
setWeight(VariableContext, VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Integer, Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(VariableContext, VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(double) - Method in class com.bayesserver.data.discovery.WeightedValue
Sets the weight (support) for the WeightedValue.getValue().
setWeight(double) - Method in class com.bayesserver.inference.DefaultEvidence
Sets a weight that can be applied to the evidence.
setWeight(double) - Method in interface com.bayesserver.inference.Evidence
Sets a weight that can be applied to the evidence.
setWeightColumn(String) - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
Sets a column that contains case weights for each record.
setWeightColumn(String) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
Sets the name of a column which contains a weight (support) for each case.
setWeightedCaseCount(double) - Method in class com.bayesserver.data.DataProgressEventArgs
Gets the number of cases read so far.
setWeightedCaseCount(double) - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
setWindowColumnName(String) - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
Sets the name of the column which will contain the window identifier.
setWindowTimeColumnName(String) - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
Sets the name of the column which will contain the window time.
size() - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
 
size() - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
 
size() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
Gets the count (support) for this cell.
size() - Method in class com.bayesserver.analysis.DSeparationTestResultCollection
 
size() - Method in class com.bayesserver.CustomPropertyCollection
 
size() - Method in class com.bayesserver.data.DataColumnCollection
Gets the number of columns in the collection.
size() - Method in class com.bayesserver.data.DataRowCollection
Gets the number of rows in the collection.
size() - Method in class com.bayesserver.data.sampling.ExcludedVariables
 
size() - Method in class com.bayesserver.inference.DefaultEvidence
Gets the count of variables with either hard, soft or temporal evidence set.
size() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
 
size() - Method in interface com.bayesserver.inference.Evidence
Gets the count of variables with either hard, soft or temporal evidence set.
size() - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
 
size() - Method in class com.bayesserver.NetworkLinkCollection
 
size() - Method in class com.bayesserver.NetworkNodeCollection
 
size() - Method in class com.bayesserver.NetworkNodeGroupCollection
 
size() - Method in class com.bayesserver.NetworkVariableCollection
Gets the number of elements contained in the NetworkVariableCollection instance.
size() - Method in class com.bayesserver.NodeDistributions
Gets the number of distributions in the container.
size() - Method in class com.bayesserver.NodeGroupCollection
Gets the number of elements contained in the NodeGroupCollection instance.
size() - Method in class com.bayesserver.NodeLinkCollection
 
size() - Method in class com.bayesserver.NodeVariableCollection
Gets the number of elements contained in the NodeVariableCollection instance.
size() - Method in class com.bayesserver.StateCollection
size() - Method in class com.bayesserver.Table
The data count in the Table.
size() - Method in class com.bayesserver.TableAccessor
Gets the count of values in the underlying Table.
size() - Method in class com.bayesserver.TableIterator
Gets the count of values in the underlying Table.
size() - Method in class com.bayesserver.VariableContextCollection
Gets the number of elements contained in the collection.
SoftEvidence - Class in com.bayesserver.inference
Helper methods for manipulating soft/virtual evidence.
sort(Network) - Static method in class com.bayesserver.TopologicalSort
Returns the nodes in a Bayesian network sorted in topological order.
SortOrder - Enum in com.bayesserver.data.discovery
The sort order of states for new discrete variables.
State - Class in com.bayesserver
Represents a state of a variable.
State() - Constructor for class com.bayesserver.State
Initializes a new instance of the State class.
State(String) - Constructor for class com.bayesserver.State
Initializes a new instance of the State class with the specified [name].
State(String, Object) - Constructor for class com.bayesserver.State
Initializes a new instance of the State class with the specified [name] and [value].
StateCollection - Class in com.bayesserver
Represents a collection of states belonging to a Variable.
StateContext - Class in com.bayesserver
Identifies a State and contextual information such as the time (zero based).
StateContext(State, Integer) - Constructor for class com.bayesserver.StateContext
Initializes a new instance of StateContext.
stateCount(int) - Method in class com.bayesserver.Table
Gets the number of states of a variable at the time this instance was constructed.
StateNotFoundAction - Enum in com.bayesserver.data
Determines the action to take when a state name or value cannot be matched to a variable state.
stateRepeat(int) - Method in class com.bayesserver.Table
Gets the number of times each state is repeated for a Variable in the Table layout.
statesCollectionChange(Variable, int, State, State, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor
For internal use.
StateValueType - Enum in com.bayesserver
The type of value represented by a State.
Stop - Interface in com.bayesserver
Interface to allow early completion of a long running task.
StructuralLearning - Interface in com.bayesserver.learning.structure
Defines methods for learning the structure (links) of a Bayesian network.
StructuralLearningOptions - Interface in com.bayesserver.learning.structure
Options governing a structural learning algorithm.
StructuralLearningOutput - Interface in com.bayesserver.learning.structure
Contains information returned from a structural learning algorithm.
StructuralLearningProgress - Interface in com.bayesserver.learning.structure
Interface to provide progress information during structural learning.
StructuralLearningProgressInfo - Interface in com.bayesserver.learning.structure
Interface to provide progress information during structural learning.
sum() - Method in class com.bayesserver.Table
Calculates the sum of all values in the Table.

T

Table - Class in com.bayesserver
Used to represent probability distributions, conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
Table(Table, boolean) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class, with the same structure as an existing [table], copying the values if requested.
Table(Table, boolean, Integer) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class, with the same structure as an existing [table], copying the values if requested, and optionally shifting any times.
Table(Variable) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with a single Variable.
Table(VariableContext) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class from a single VariableContext.
Table(List<Variable>, Integer) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with the specified variables, at an optional time.
Table(List<Variable>, Integer, HeadTail) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with the specified variables, at an optional time.
Table(VariableContextCollection) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with the variables specified in [variableContexts].
Table(VariableContext[]) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(List<VariableContext>) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(List<VariableContext>, HeadTail) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(Variable...) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with the specified variables.
Table(VariableContext[], int) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with [count] variable contexts taken from [buffer].
Table(VariableContext[], int, HeadTail) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with [count] variable contexts taken from [buffer].
Table(Node, Integer) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with the specified node variable at the specified time.
Table(Variable, Integer) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with a single Variable and time.
Table(Node...) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with all the variables from the supplied nodes.
Table(Node[], HeadTail) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with all the variables from the supplied nodes.
Table(Table) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class, copying the [table] passed in.
Table(Table, Integer) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class, copying the [table] passed in, however adjusting any times by the [timeShift].
Table(Node) - Constructor for class com.bayesserver.Table
Initializes a new instance of the Table class with the specified node variables.
Table.MarginalizeLowMemoryOptions - Class in com.bayesserver
Table.MarginalizeLowMemoryOptions() - Constructor for class com.bayesserver.Table.MarginalizeLowMemoryOptions
 
Table.MaxValue - Class in com.bayesserver
 
Table.NonZeroValues - Interface in com.bayesserver
Used to report non zero table values.
TableAccessor - Class in com.bayesserver
Allows random access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in Table.getSortedVariables().
TableAccessor(Table, Variable[]) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableAccessor(Table, List<VariableContext>) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableAccessor(Table, VariableContextCollection) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableAccessor(Table, Node[], Integer[]) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the node variables.
TableAccessor(Table, Variable[], Integer[]) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables at specified times.
TableAccessor(Table, List<Variable>, List<Integer>) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables at specified times.
TableAccessor(Table, Node[]) - Constructor for class com.bayesserver.TableAccessor
Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the node variables.
TableIterator - Class in com.bayesserver
Allows sequential access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in Table.getSortedVariables().
TableIterator(Table, Variable[]) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables.
TableIterator(Table, Variable[], Integer[]) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables at specified times.
TableIterator(Table, List<Variable>, List<Integer>) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables at specified times.
TableIterator(Table, Node[], Integer[]) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
TableIterator(Table, VariableContextCollection) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables.
TableIterator(Table, Node[]) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
TableIterator(Table, List<VariableContext>) - Constructor for class com.bayesserver.TableIterator
Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
takeSample(Evidence, Random, DataSamplingOptions) - Method in class com.bayesserver.data.sampling.DataSampler
Generates sample data from the Bayesian network or Dynamic Bayesian network.
TANLinkOutput - Class in com.bayesserver.learning.structure
Contains information about a new link learnt using the com.bayesserver.learning.structure.tan.TANStructuralLearning algorithm.
TANStructuralLearning - Class in com.bayesserver.learning.structure
A structural learning algorithm for Bayesian networks based on the Tree augmented naive Bayes (TAN) algorithm.
TANStructuralLearning() - Constructor for class com.bayesserver.learning.structure.TANStructuralLearning
 
TANStructuralLearningOptions - Class in com.bayesserver.learning.structure
Options for structural learning with the com.bayesserver.learning.structure.tan.TANStructuralLearning class.
TANStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.TANStructuralLearningOptions
 
TANStructuralLearningOutput - Class in com.bayesserver.learning.structure
Contains information returned from the com.bayesserver.learning.structure.tan.TANStructuralLearning algorithm.
TANStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
Progress information returned from the TAN structural learning algorithm.
TemporalReaderOptions - Class in com.bayesserver.data
Options that apply to the reading of temporal data.
TemporalReaderOptions(String, String, TimeValueType) - Constructor for class com.bayesserver.data.TemporalReaderOptions
Initializes a new instance of the TemporalReaderOptions class.
TemporalReadInfo - Class in com.bayesserver.data
Provides information about a temporal record.
TemporalReadInfo(Integer, Object, DataRecord) - Constructor for class com.bayesserver.data.TemporalReadInfo
Initializes a new instance of the TemporalReadInfo class.
TemporalType - Enum in com.bayesserver
The node type for networks that include temporal/sequential support.
test(Evidence) - Method in class com.bayesserver.analysis.InSampleAnomalyDetection
Determines whether a record is anomalous.
test(DataPartitioning, CrossValidationNetwork) - Method in interface com.bayesserver.data.CrossValidationActions
A user supplied function to test the network on a test partitioning of the data.
TimeSeriesMode - Enum in com.bayesserver.learning.parameters
Determines how time series distributions are learned.
timeShift(int) - Method in class com.bayesserver.CLGaussian
Shifts any times associated with the distribution variables by the specified number of time units.
timeShift(int) - Method in interface com.bayesserver.Distribution
Shifts any times associated with the distribution variables by the specified number of time units.
timeShift(int) - Method in class com.bayesserver.Table
Shifts any times associated with the table variables by the specified number of units.
TimeValueType - Enum in com.bayesserver.data
The type of values stored in a time column.
TopologicalSort - Class in com.bayesserver
Contains methods to sort nodes in a Bayesian network in topological order.
toString() - Method in class com.bayesserver.CLGaussian
toString() - Method in class com.bayesserver.data.discovery.DiscretizationOptions
toString() - Method in class com.bayesserver.inference.QueryDistribution
Returns a String that represents this instance.
toString() - Method in class com.bayesserver.Interval
toString() - Method in class com.bayesserver.learning.parameters.InitializationOptions
toString() - Method in class com.bayesserver.learning.parameters.Priors
Returns a String that represents this instance.
toString() - Method in class com.bayesserver.Node
Returns the name of the node, or an empty string if the name is null.
toString() - Method in class com.bayesserver.State
Returns the name of the state, or an empty string if the name is null.
toString() - Method in class com.bayesserver.Table
toString() - Method in class com.bayesserver.Variable
Returns the name of the variable, or an empty string if the name is null.
toString() - Method in class com.bayesserver.VariableContextCollection
Returns a String that represents the current Object.
TreeQuery - Class in com.bayesserver.inference
Contains methods to determine properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
TreeQueryOptions - Class in com.bayesserver.inference
Options which affect the calculation performed by a TreeQuery.
TreeQueryOptions() - Constructor for class com.bayesserver.inference.TreeQueryOptions
Initializes a new instance of the TreeQueryOptions class.
TreeQueryOptions(QueryOptions) - Constructor for class com.bayesserver.inference.TreeQueryOptions
Initializes a new instance of the TreeQueryOptions class, copying options from another instance implementing QueryOptions.
TreeQueryOutput - Class in com.bayesserver.inference
Contains information output by a TreeQuery.
twoWay(Evidence, State, ParameterReference, ParameterReference) - Method in class com.bayesserver.analysis.SensitivityToParameters
Calculates how a hypothesis varies based on changes to two parameters.

U

unroll(Network, int, UnrollOptions) - Static method in class com.bayesserver.Unroller
Unrolls the specified Dynamic Bayesian network into the equivalent Bayesian network.
Unroller - Class in com.bayesserver
Unrolls a Dynamic Bayesian network into the equivalent Bayesian network.
UnrollOptions - Class in com.bayesserver
Options governing the unrolling of a Dynamic Bayesian network.
UnrollOptions() - Constructor for class com.bayesserver.UnrollOptions
 
UnrollOutput - Class in com.bayesserver
UnrollOutput.NodeTime - Class in com.bayesserver
Identifies a node and related time.
UnrollOutput.VariableTime - Class in com.bayesserver
Identifies a variable and related time.
update(DiscretizeProgressInfo) - Method in interface com.bayesserver.data.discovery.DiscretizeProgress
Progress updates from a discretization algorithm.
update(VariableGeneratorProgressInfo) - Method in interface com.bayesserver.data.discovery.VariableGeneratorProgress
Progress updates from the Variable Generator algorithm.
update(ParameterLearningProgressInfo) - Method in interface com.bayesserver.learning.parameters.ParameterLearningProgress
Progress update, containing information about the last iteration.
update(StructuralLearningProgressInfo) - Method in interface com.bayesserver.learning.structure.StructuralLearningProgress
Progress updates from the structural learning algorithm.

V

validate(String) - Static method in class com.bayesserver.License
Validates the library.
validate(ValidationOptions) - Method in class com.bayesserver.Network
Validates that the Bayesian network is correctly specified.
validateDistribution(Distribution, NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions
Checks that a distribution is correctly specified for a particular temporal order.
validateDistribution(Distribution, NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
Checks that a distribution is correctly specified for a particular temporal order.
validateTrialSession() - Static method in class com.bayesserver.Network
Evaluation version only.
ValidationOptions - Class in com.bayesserver
Represents options that govern the validation of a network.
ValidationOptions() - Constructor for class com.bayesserver.ValidationOptions
 
value(int, double) - Method in interface com.bayesserver.Table.NonZeroValues
Called for each non zero value in the table.
valueOf(String) - Static method in enum com.bayesserver.analysis.AutoInsightJSDivergence
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.AutoInsightKLDivergence
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.DSeparationCategory
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.ImpactSubsetMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.LogLikelihoodAnalysisSubsetMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.ValueOfInformationKind
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.CollectionAction
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.ColumnValueType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.CrossValidationCombineMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.DataPartitionMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.discovery.DiscretizationMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.discovery.SortOrder
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.EmptyStringAction
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.StateNotFoundAction
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.TimeValueType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.HeadTail
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.DecisionAlgorithm
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.EvidenceType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.QueryComparison
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.QueryDistance
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.QueryEvidenceMode
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.IntervalEndPoint
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.ConvergenceMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.DecisionPostProcessingMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.DiscretePriorMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.DistributionMonitoring
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.InitializationMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.TimeSeriesMode
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.structure.LinkConstraintFailureMode
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.structure.LinkConstraintMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.structure.ScoreMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.NodeDistributionKind
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.NoisyOrder
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.NoisyType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.PropagationMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.StateValueType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.statistics.LogarithmBase
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.TemporalType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.VariableKind
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.VariableValueType
Returns the enum constant of this type with the specified name.
ValueOfInformation - Class in com.bayesserver.analysis
Contains methods to determine what new evidence is most likely to reduce the uncertainty of a variable.
ValueOfInformationKind - Enum in com.bayesserver.analysis
The type of value of information statistic calculated.
ValueOfInformationOptions - Class in com.bayesserver.analysis
Options for calculating ValueOfInformation.
ValueOfInformationOptions() - Constructor for class com.bayesserver.analysis.ValueOfInformationOptions
Initializes a new instance of the ValueOfInformationOptions class.
ValueOfInformationOutput - Class in com.bayesserver.analysis
Contains the results of the tests carried out using ValueOfInformation.
ValueOfInformationTestOutput - Class in com.bayesserver.analysis
Contains information about a variable tested via ValueOfInformation.
values() - Static method in enum com.bayesserver.analysis.AutoInsightJSDivergence
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.AutoInsightKLDivergence
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.DSeparationCategory
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.ImpactSubsetMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.LogLikelihoodAnalysisSubsetMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.ValueOfInformationKind
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.CollectionAction
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.ColumnValueType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.CrossValidationCombineMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.DataPartitionMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.discovery.DiscretizationMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.discovery.SortOrder
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.EmptyStringAction
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.StateNotFoundAction
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.TimeValueType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.HeadTail
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.DecisionAlgorithm
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.EvidenceType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.QueryComparison
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.QueryDistance
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.QueryEvidenceMode
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.IntervalEndPoint
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.ConvergenceMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.DecisionPostProcessingMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.DiscretePriorMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.DistributionMonitoring
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.InitializationMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.TimeSeriesMode
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.structure.LinkConstraintFailureMode
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.structure.LinkConstraintMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.structure.ScoreMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.NodeDistributionKind
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.NoisyOrder
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.NoisyType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.PropagationMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.StateValueType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.statistics.LogarithmBase
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.TemporalType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.VariableKind
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.VariableValueType
Returns an array containing the constants of this enum type, in the order they are declared.
Variable - Class in com.bayesserver
Represents a discrete or continuous random variable.
Variable() - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class, with VariableValueType discrete and zero states.
Variable(String) - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class, with VariableValueType discrete, zero states, and the specified name.
Variable(String, VariableValueType, VariableKind) - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class with the specified name, kind and value type.
Variable(String, VariableValueType) - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class with the specified name and value type.
Variable(String, int) - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class, with VariableValueType discrete and the specified [name] and adds the number of states specified in [states].
Variable(String, String[]) - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class, with VariableValueType discrete and the specified name and adds the states specified in [states].
Variable(String, State...) - Constructor for class com.bayesserver.Variable
Initializes a new instance of the Variable class, with VariableValueType discrete and the specified name