All Classes

Class	Description
Abduction	Performs abduction which is one of the steps in 'counterfactual analysis'.
AbductionOptions	Options for Abduction.update(com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, java.util.List<com.bayesserver.Variable>, com.bayesserver.causal.AbductionOptions).
AdjustmentNotFoundException	Raised by a causal inference algorithm when an adjustment set cannot be found.
AdjustmentSet	The set of nodes that an estimation procedure must adjust for (condition on) to avoid any bias in the results.
AdjustmentSetNode	Represents a node in an adjustment set.
ArcReversal	Contains methods to reverse the direction of a Link, known as arc reversal.
AssignedDefinition	Identifies the node that is assigned to a clique in a Junction Tree.
Association	Calculates the strength between pairs of variables or sets of variables.
AssociationOptions	Options that affect the link strength algorithm.
AssociationOutput	Contains the results of an Association analysis.
AssociationPair	Defines two sets of variables to be analyzed by the Association algorithm.
AssociationPairOutput	Contains the results of the association calculations between two sets of variables.
AutoInsight	Uses comparison queries to automatically derive insight about a target variable from a trained network.
AutoInsightJSDivergence	Determines the type of Jensen Shannon divergence calculations, if any, performed during an auto insight analysis.
AutoInsightKLDivergence	Determines the type of KL divergence calculations, if any, performed during an auto insight analysis.
AutoInsightOptions	Options that affect auto-insight calculations.
AutoInsightOutput	Contains the results obtained from AutoInsight.
AutoInsightSamplingOptions	Options that affect any sampling required during auto-insight calculations.
AutoInsightStateOutput	Contains the results obtained from AutoInsight for each test variable.
AutoInsightStateOutputCollection	Represents a collection of AutoInsightStateOutput instances.
AutoInsightVariableOutput	Represents the output obtained from AutoInsight for a test variable.
AutoInsightVariableOutputCollection	Represents a collection of AutoInsightVariableOutput instances.
BackdoorCriterion	Uses the 'Backdoor Criterion' to identify 'adjustment sets', that if found can be used to estimate the causal effect using theBackdoorInference.
BackdoorCriterionOptions	Options for BackdoorCriterion.
BackdoorCriterionOutput	The output from the Backdoor criterion, including any 'adjustment sets' identified.
BackdoorGraph	Methods for constructing the Backdoor graph or proper Backdoor graph from a Bayesian network.
BackdoorGraphOptions	Options for 'Backdoor graph' construction.
BackdoorInference	Estimates the causal effect, using the 'Backdoor Adjustment' formula to avoid confounding bias.
BackdoorInferenceFactory	Uses the factory design pattern to create inference related objects for the Backdoor adjustment algorithm.
BackdoorMethod	The sets for the Backdoor criterion to find.
BackdoorQueryOptions	Options for BackdoorInference
BackdoorQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
BackdoorValidationOptions	Options for Backdoor Criterion validation, which can be used to test whether adjustment sets are valid.
Bounds	Stores the position and size of an element.
Cancellation	Interface for cancelling long running operations.
CausalEffectKind	The type of causal effect to identify or estimate.
CausalInferenceBase	Base class for Causal inference engines used by internal algorithms.
CausalNode	Represents a reference to any node in a Causal model, for example a treatment (X), an outcome (Y), an unobserved node (U).
CausalObservability	Gets or sets the observability of a node which is causal.
CausalQueryOptionsBase	Base class for causal query options.
CausalQueryOutput	Additional outputs specific to causal queries.
CausalQueryOutputBase	Base class for causal algorithm output.
ChowLiuLinkOutput	Contains information about a new link learnt using the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning algorithm.
ChowLiuStructuralLearning	A structural learning algorithm for Bayesian networks based on the Chow-Liu algorithm.
ChowLiuStructuralLearningOptions	Options for structural learning with the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning class.
ChowLiuStructuralLearningOutput	Contains information returned from the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning algorithm.
ChowLiuStructuralLearningProgressInfo	Progress information returned from the Chow-Liu structural learning algorithm.
CLGaussian	Represents a Conditional Linear Gaussian probability distribution.
CliqueDefinition	The definition of a clique in a junction tree, without the instantiation of the distribution.
ClusterCount	Methods to determine the number of clusters (discrete states of a latent variable).
ClusterCountActions	Actions which the caller must implement to use ClusterCount.
ClusterCountOptions	Options used by ClusterCount.
ClusterCountOutput	Output information returned from ClusterCount.
Clustering	Discretizes continuous data in bins, using a probabilistic clustering algorithm.
ClusteringLinkOutput	Contains information about a new link learnt using the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning algorithm.
ClusteringStructuralLearning	A structural learning algorithm for a cluster model (a.k.a mixture model).
ClusteringStructuralLearningOptions	Options for structural learning with the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning class.
ClusteringStructuralLearningOutput	Contains information returned from the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning algorithm.
ClusteringStructuralLearningProgressInfo	Progress information returned from the Clustering structural learning algorithm.
ClusterScore	Contains the results of a cluster configuration returned from ClusterCount.
CollectionAction	Specifies how the collection is changed.
ColumnValueType	Specifies the type of data in a column.
CombinationAction	Interface to receive combinations from the Combinations.enumerate(java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.CombinationAction, com.bayesserver.analysis.CombinationOptions) method.
CombinationOptions	Determines which combinations are generated by Combinations.
Combinations	Generates the available state combinations for a set of variables or counts.
ConfusionMatrix	Calculates a confusion matrix for a network which is used to predict discrete values (classification).
ConfusionMatrixCell	Contains statistics about a cell in a ConfusionMatrix.
ConstraintNotSatisfiedException	Exception raised when parameter tuning attempts to solve for a constraint that cannot be satisfied by the change(s) in parameters.
ConstraintSatisfiedException	Exception raised when parameter tuning attempts to solve for a constraint that is already true.
ConvergenceException	Exception raised when an iterative inference algorithm fails to converge to within a given tolerance.
ConvergenceMethod	The method used to determine whether learning has converged.
Correlation	Methods to convert covariance matrices to correlation matrices.
CrossValidation	Allows test metrics/scores to be calculated using cross validation.
CrossValidationActions	Actions which the caller must implement to use Cross Validation.
CrossValidationCombineMethod	Ways of combining cross validation test results to form an overall cross validation score.
CrossValidationNetwork	The result of learning on a single cross validation training partitioning.
CrossValidationOutput	Details of a Cross-Validation partition.
CrossValidationScore	Interface for cross validation scores.
CrossValidationTestResult	Interface for cross validation test results.
CustomProperty	Stores a custom property.
CustomPropertyCollection	Stores custom properties for a variety of objects.
Dag	Includes methods for testing whether a network is a Directed Acyclic Graph (DAG).
DatabaseDataReaderCommand	Provides a default implementation of DataReaderCommand for reading databases.
DataColumn	Class that represents an memory column of data.
DataColumnCollection	Represents a collection of columns in a DataTable, a simple in-memory data store.
DataIOException	Raised when an error occurs reading data from or writing data to a database, a file or other source.
DataPartition<T>	Interface used by distributed processes that read data.
DataPartitioning	Determines how data is partitioned.
DataPartitionMethod	Determines whether data is included or excluded from a DataPartitioning.
DataProgress	Reports progress on the number of cases read.
DataProgressEventArgs	Used to provide progress on how many cases have been read.
DataReader	Interface for reading data row by row.
DataReaderCommand	Interface used by EvidenceReader in order to read data multiple times.
DataReaderCommandFiltered	Wraps an existing data reader command while filtering records.
DataReaderFilter	Interface to determine whether records should be filtered in a data reader.
DataReaderFiltered	Wraps an existing data reader while filtering records.
DataRecord	Interface for reading the values from a row of data.
DataRow	Represents a row of data in a DataTable, a simple in-memory data store.
DataRowCollection	A collection of rows in a DataTable, a simple in-memory data store.
DataSampler	Generates samples from a Bayesian network or Dynamic Bayesian network.
DataSamplingOptions	Options for data sampling.
DataTable	A simple in memory data structure which can be used as an alternative to a data store (such as a database).
DataTableDataReaderCommand	A DataReaderCommand backed by a DataTable.
DataTableEvidenceReaderCommandFactory	A default implementation of EvidenceReaderCommandFactory based on a DataTable and a simple partitioning scheme based on a partition column.
DataTableReader	Allows a DataTable to be read as a DataReader.
DecisionAlgorithm	The type of algorithm to use when a network has decision nodes.
DecisionPostProcessingMethod	The type of post processing to be applied to the distributions of decision nodes at the end of parameter learning.
DecomposeOptions	Options used by the Decomposer class.
DecomposeOutput	Contains information returned by Decomposer.decompose(com.bayesserver.Network, com.bayesserver.DecomposeOptions).
Decomposer	Contains methods to decompose nodes with multiple variables into their single variable equivalents.
DefaultCancellation	Class for canceling long running operations.
DefaultCrossValidationNetwork	Default basic implementation of ICrossValidationNetwork.
DefaultCrossValidationScore	A default simple implementation of ICrossValidationScore.
DefaultCrossValidationTestResult	A simple default implementation of CrossValidationTestResult.
DefaultDataReader	Reads and validates non temporal and/or temporal data.
DefaultEvidence	Represents the evidence, or case data (e.g.
DefaultEvidenceReader	Provides a default implementation of EvidenceReader, used in Bayes Server for tasks such as parameter learning.
DefaultEvidenceReaderCommand	Creates instances of EvidenceReader on demand.
DefaultQueryDistributionCollection	The collection of distributions to be calculated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
DefaultQueryFunctionCollection	The collection of functions to be evaluated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
DefaultReadOptions	Provides a default implementation of ReadOptions.
DesignEvidenceKind	The type of evidence the optimizer should use.
DesignState	An input to the optimization algorithm.
DesignVariable	Specifies on or more inputs to the optimization algorithm.
DiscretePriorMethod	The type of discrete prior to use for discrete distributions during parameter learning.
DiscretizationAlgoOptions	Options for a discretization algorithm.
DiscretizationColumn	Identifies a column of data and how it is to be discretized.
DiscretizationInfo	Discretization information for column of data, returned from a discretization algorithm.
DiscretizationMethod	The method (algorithm) to use for discretization of continuous data.
DiscretizationOptions	Options that determine whether and how continuous data should be discretized.
Discretize	Interface which a discretization algorithm must implement.
DiscretizeProgress	Interface to provide progress information during discretization.
DiscretizeProgressInfo	Interface to provide progress information during discretization.
DisjunctiveCauseCriterion	Validates inputs for the Disjunctive cause adjustment.
DisjunctiveCauseCriterionOptions	Options for Disjunctive-cause Criterion validation.
DisjunctiveCauseCriterionOutput	The output from the Disjunctive-cause criterion, which is simply an adjustment set which includes all causes of treatments (X) or causes of outcomes (Y) or causes of both.
DisjunctiveCauseInference	Estimates the causal effect, using the 'Disjunctive Cause Criterion' adjustment formula to avoid confounding bias.
DisjunctiveCauseInferenceFactory	Uses the factory design pattern to create inference related objects for the Disjunctive cause algorithm.
DisjunctiveCauseQueryOptions	Options for DisjunctiveCauseInference.
DisjunctiveCauseQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
DisjunctiveCauseSet	Identifies sets of nodes used by the Disjunctive Cause Criterion algorithm.
DisjunctiveCauseSetNode	Represents a node in a set used by the Disjunctive Cause Criterion algorithm.
DisjunctiveCauseValidationOptions	Options for Disjunctive-cause criterion validation.
DistributedMapperContext	Contains information used during distributed parameter learning.
Distributer<T>
DistributerContext	Contains contextual information about the process/iteration being distributed.
Distribution	Interface specifying the required methods and properties for a probability distribution.
DistributionExpression	Base interface for expressions that generate distributions.
DistributionMonitoring	Indicates which distribution to monitor during learning.
DistributionSpecification	Identifies a node's distribution to learn, and options for learning.
DSeparation	Contains methods to calculate D-Separation.
DSeparationCategory	The result of a D-Separation test.
DSeparationOptions	Options for calculating D-Separation.
DSeparationOutput	Contains the results of a test for D-Separation.
DSeparationTestResult	The result of a D-Separation check for a test node.
DSeparationTestResultCollection	Collection of D-Separation test results.
EffectsAnalysis	Calculates the causal effect on a target, varying for different treatment values.
EffectsAnalysisOptions	Options for an effects analysis.
EffectsAnalysisOutput	The results of an effects analysis.
EffectsAnalysisOutputItem	The result of an effects analysis for a particular treatment value.
EliminationDefinition	Identifies a node that is eliminated during exact inference.
EliminationDefinitionCollection	A list of elminated nodes during inference.
EmpiricalDensity	Represents an empirical density function, which can represent arbitrary univariate distributions.
EmptyStringAction	Determines the action to take when an empty string is encountered.
Entropy	Calculates entropy, joint entropy or conditional entropy, which can be used to determine the uncertainty in the states of a discrete distribution.
EqualFrequencies	Discretizes continuous data in bins, such that each bin contain a similar number of data points.
EqualIntervals	Discretizes continuous data in bins, such that the bins have equal size.
Evidence	Represents the evidence, or case data (e.g.
EvidencePartition<T>	Interface used by distributed processes that read evidence.
EvidenceReader	A data set iterator, that can be read multiple times.
EvidenceReaderCommand	Interface used to create instances of EvidenceReader.
EvidenceReaderCommandFactory	Creates evidence reader commands, for repeated iterating of a data set/partition of a data set.
EvidenceReaderEventArgs	Contains a reference to a reader created by a reader command.
EvidenceType	The type of evidence for a variable.
EvidenceTypes	Provides information about the type of evidence on a variable as well as whether it is an intervention (do operator) or not.
ExcludedVariables	Set of variables which should be excluded from an operation, such as missing data generation.
ExecuteEvidenceReader	Used to receive notification of a new Evidence reader being created from an evidence reader command.
Expression	Base interface for expressions.
ExpressionDistribution	Determines what happens when an expression is set on a node distribution.
ExpressionException	Exception raised during lexing, parsing or evaluation of an expression.
ExpressionReturnType	The type of value returned from an expression.
FeatureSelection	Contains methods to determine which variables are likely to be good features (predictors) or not.
FeatureSelectionOptions	Options governing the tests carried out to determine whether variables are likely to be features (predictors) of a target variable.
FeatureSelectionOutput	Contains information returned by FeatureSelection.detect(java.util.List<com.bayesserver.Variable>, com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.Variable, com.bayesserver.learning.structure.FeatureSelectionOptions) about feature selection tests.
FeatureSelectionTest	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.
FrontDoorCriterion	Uses the 'Front-door Criterion' to identify any sets of valid front-door nodes, that if found can be used to estimate the causal effect using theFrontDoorInference.
FrontDoorCriterionOptions	Options for FrontDoorCriterion.
FrontDoorCriterionOutput	The output from the Front-door criterion, including any sets of 'front-door nodes' identified.
FrontDoorInference	Estimates the causal effect, using the 'Front-door Adjustment' formula to avoid confounding bias.
FrontDoorInferenceFactory	Uses the factory design pattern to create inference related objects for the Front-door adjustment algorithm.
FrontDoorQueryOptions	Options for FrontDoorInference
FrontDoorQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
FrontDoorSet	Front-door nodes used by the front-door adjustment.
FrontDoorSetNode	Represents a front-door node used by the front-door adjustment, and can be identified by the front-door criterion.
FrontDoorValidationOptions	Options for Front-door Criterion validation, which can be used to test whether the front-door nodes are valid and the pair of associated 'adjustment sets' are also valid..
FunctionException	Exception raised during the evaluation of a function expression.
FunctionVariableExpression	An expression that can be used in a function node/variable.
GeneticOptimizer	A genetic algorithm optimizer.
GeneticOptimizerOptions	Options governing the behaviour of the com.bayesserver.optimization.genetic.GeneticOptimizer algorithm.
GeneticOptimizerOutput	Contains the results from the genetic optimization algorithm.
GeneticOptimizerProgressInfo	Contains progress information sent from the genetic optimization algorithm.
GeneticOptionsBase	Base class for common Genetic algorithm options.
GeneticSimplification	An algorithm that attempts to simply the evidence found by an optimizer.
GeneticSimplificationOptions	Options for the genetic simplifcation algorithm.
GeneticSimplificationOutput	Contains the results from the genetic simplifcation algorithm.
GeneticTerminationOptions	Termination options for the genetic optimization algorithm.
HeadTail	Indicates whether a variable is marked as head or tail in a distribution.
HierarchicalLinkOutput	Contains information about a new link learnt using the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning algorithm.
HierarchicalStructuralLearning	A structural learning algorithm for Bayesian networks that groups subsets of nodes into a hierarchy.
HierarchicalStructuralLearningOptions	Options for structural learning with the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning class.
HierarchicalStructuralLearningOutput	Contains information returned from the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning algorithm.
HierarchicalStructuralLearningProgressInfo	Progress information returned from the Hierarchical structural learning algorithm.
HistogramDensity	Represents an empirical density function built from a histogram, which can represent arbitrary univariate distributions.
HistogramDensityItem	Information about each interval in the histogram density.
HistogramDensityOptions	Options for learning a histogram based empirical density.
Identification	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOptions	Options for classes that implement Identification
IdentificationOutput	Output for classes that implement Identification
Impact	Analyzes the impact of evidence.
ImpactAction	Interface to receive impact outputs from the Impact.calculate(com.bayesserver.Network, com.bayesserver.Variable, com.bayesserver.State, com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.ImpactOptions) method.
ImpactHypothesisOutput	Output information about the hypothesis variable/state from an Impact analysis.
ImpactOptions	Options affecting how Impact analysis calculations are performed.
ImpactOutput	Contains the results of an Impact analysis.
ImpactOutputItem	The output from an impact analysis, for a particular subset of evidence.
ImpactSubsetMethod	Determines how subsets are determined during impact analysis.
InconsistentEvidenceException	Exception raised when either inconsistent evidence is detected, or underflow has occurred.
InconsistentEvidenceMode	Determines when an InconsistentEvidenceException is raied.
IndependenceOptions	Options governing independence and conditional independence tests.
IndirectGraph	Methods for constructing the 'Indirect graph' from a Bayesian network.
IndirectGraphOptions	Options for 'Indirect graph' construction.
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	Uses the factory design pattern to create inference related objects for inference algorithms.
InitializationMethod	Determines the algorithm used to initialize distributions during parameter learning.
InitializationOptions	Options governing the initialization of distributions at the start of parameter learning.
InSampleAnomalyDetection	Detects in-sample anomalies in a data set.
InSampleAnomalyDetectionActions	Actions which the caller must implement to use InSampleAnomalyDetection.
InSampleAnomalyDetectionOptions	Options used by InSampleAnomalyDetection.
InSampleAnomalyDetectionOutput	Output used by InSampleAnomalyDetection.
Interval<T extends Comparable>	An interval, defined by a minimum and maximum with respective open or closed endpoints.
IntervalEndPoint	The type of end point for an interval.
IntervalStatistics	Calculates statistics such as mean and variance for discretized variables, i.e.
InterventionType	Determines whether evidence is an intervention (do operator) or not.
InvalidNetworkException	Raised when a network has not been correctly specified.
JensenShannon	Methods for computing the Jensen Shannon divergence, which measures the similarity between probability distributions.
JunctionTreeNodeDefinition	A junction tree node, which can be either a clique or a sepset.
JunctionTreesDefinition	A jumction tree or junction trees.
KullbackLeibler	Calculate the Kullback–Leibler divergence between 2 distributions with the same variables, D(P||Q).
License	Provides license validation.
LiftChart	Represents a lift chart, used to measure predictive performance.
LiftChartPoint	Represents an XY coordinate in a lift chart.
LikelihoodSamplingInference	An approximate probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, based on Likelihood Sampling.
LikelihoodSamplingInferenceFactory	Uses the factory design pattern to create inference related objects for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryLifecycleBegin	Query lifecycle begin implementation for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryLifecycleEnd	Query end lifecycle implementation for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
LikelihoodSamplingQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
Link	Represents a directed link in a Bayesian network.
LinkConstraint	Defines a constraint on a link between two nodes during structural learning.
LinkConstraintCollection	A collection of link constraints.
LinkConstraintFailureMode	Determines the action taken if a link constraint cannot be honoured.
LinkConstraintMethod	Determines how a link is constrained.
LinkOutput	Contains information about a link returned from a structural learning algorithm.
LogarithmBase	Determines the base of the logarithm to use during calculations such as mutual information.
LogLikelihoodAnalysis	Analyzes the log-likelihood for different evidence subsets.
LogLikelihoodAnalysisAction	Interface to receive Log-Likelihood analysis outputs from the LogLikelihoodAnalysis.calculate(com.bayesserver.Network, com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.LogLikelihoodAnalysisOptions) method.
LogLikelihoodAnalysisBaselineOutput	Output information about the log-likelihood from a log-likelihood analysis.
LogLikelihoodAnalysisOptions	Options affecting how Log-Likelihood analysis calculations are performed.
LogLikelihoodAnalysisOutput	Contains the results of a Log-Likelihood analysis.
LogLikelihoodAnalysisOutputItem	The output from a Log-Likelihood analysis, for a particular subset of evidence.
LogLikelihoodAnalysisSubsetMethod	Determines how subsets are determined during a Log-Likelihood analysis.
LoopyBeliefInference	An approximate but deterministic probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks based on Loopy Belief Propagation.
LoopyBeliefInferenceFactory	Uses the factory design pattern to create inference related objects for the Loopy Belief algorithm.
LoopyBeliefQueryLifecycleBegin	Query lifecycle begin implementation for the Loopy Belief algorithm.
LoopyBeliefQueryLifecycleEnd	Query end lifecycle implementation for the Loopy Belief algorithm.
LoopyBeliefQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
LoopyBeliefQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
MultipleIterator	Provides methods to iterate over multiple distributions.
MultipleIterator.Combination
MutualInformation	Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
NameValuesReader	Interface for reading name/value pairs.
NameValuesWriter	Interface for writing name/value pairs.
NestedDataReader	Allows nested table to be read using a DefaultDataReader.
NestedReadInfo	Provides information about a nested table record.
Network	Represents a Bayesian Network, or a Dynamic Bayesian Network.
NetworkLinkCollection	Represents the collection of directed links maintained by the Network class.
NetworkMonitor	For internal use.
NetworkNodeCollection	Represents the collection of Network.getNodes() maintained by the Network class.
NetworkNodeGroupCollection	A collection of groups.
NetworkVariableCollection	Represents a read-only collection of variables that belong to a network.
Node	Represents a node with one or more variables in a Bayesian network.
NodeDistributionExpressions	Represents any distribution expressions assigned to a Node.
NodeDistributionExpressions.DistributionExpressionOrder	Identifies a distribution expression and its temporal order.
NodeDistributionKey	Identifies a distribution assigned or to be assigned to a node.
NodeDistributionKind	The kind of distribution, such as a standard Probability or Experience table.
NodeDistributionOptions	Options that apply to all distributions of a particular node.
NodeDistributions	Represents the distributions assigned to a Node.
NodeDistributions.DistributionOrder	Identifies a distribution and its temporal order.
NodeGroup	Allows nodes to be assigned to one or more groups.
NodeGroupCollection	Represents the collection of groups a node belongs to.
NodeLinkCollection	Represents a read-only collection of links.
NodeSet	A set of nodes.
NodeSetItem	Represents a node in a set.
NodeVariableCollection	Represents the collection of variables belonging to a
NoisyOrder	Determines the order in which the states of a parent of a noisy node increasingly affect the noisy states.
NoisyType	Identifies the noisy node type, if any.
NotInDomainException	Raised when the arguments to a mathematic function are not in the domain of the function (undefined).
NotSpdException	Raised when a matrix is not positive definite.
Objective	Defines the target variable or state that you wish to maximize or minimize.
ObjectiveKind	The type of optimization to carry out, such as Minimization or Maximization.
OnlineLearning	Adapts the parameters of a Bayesian network, using Bayesian statistics.
OnlineLearningOptions	Options for online learning (adaptation using Bayesian statistics).
OptimizationWarning	A warning generated by an optimization algorithm
Optimizer	Interface required by optimization algorithms.
OptimizerOptions	Optimizer options that are common across all algorithms.
OptimizerOutput	Contains output common to optimization algorithms.
OptimizerProgress	Interface to provide progress information during optimization.
OptimizerProgressInfo	Interface to provide progress information during optimization.
ParameterCounter	Contains methods to determine the number of parameters in a Bayesian network or distribution.
ParameterCountOptions	Options for ParameterCounter.
ParameterLearning	Learns the parameters of Bayesian networks and Dynamic Bayesian networks, from data.
ParameterLearningOptions	Options governing parameter learning.
ParameterLearningOutput	Contains summary information returned by ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
ParameterLearningProgress	Interface to provide progress information during parameter learning.
ParameterLearningProgressInfo	Provides progress information during ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
ParameterReference	References a parameter in a node distribution.
ParameterTuning	Calculates how a parameter can be updated so that the resulting value of a hypothesis is within a given range.
ParameterTuningOneWay	Represents the result of one way parameter tuning.
PartitionDataReaderFilter	A data reader filter based on an integer column, which can contain ids or a zero based partition identifier.
PCLinkOutput	Contains information about a new link learnt using the com.bayesserver.learning.structure.pc.PCStructuralLearning algorithm.
PCStructuralLearning	A structural learning algorithm for Bayesian networks based on the PC algorithm.
PCStructuralLearningOptions	Options for structural learning with the com.bayesserver.learning.structure.pc.PCStructuralLearning class.
PCStructuralLearningOutput	Contains information returned from the com.bayesserver.learning.structure.pc.PCStructuralLearning algorithm.
PCStructuralLearningProgressInfo	Progress information returned from the PC structural learning algorithm.
Priors	Contains parameters used to avoid boundary conditions during learning.
PropagationMethod	The propagation method used during inference.
QueryComparison	Determines whether and how queried values (e.g.
QueryDistance	Type of distance to calculate for a query.
QueryDistribution	Defines a distribution to be queried in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryDistributionCollection	The collection of distributions to be calculated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryEvidenceMode	Determines how predictions on variables with evidence are performed.
QueryExpression	Base interface for expressions that are evaluated at query time.
QueryFunction	Defines a function to be evaluated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryFunctionCollection	Collection of functions to be evaluated at query time, after any query distributions have been calculated.
QueryFunctionOutput	A class whose value holds the result of a function evaluation, populated during a query.
QueryLifecycle	Allows callers to hook into the query lifecycle of an inference engine.
QueryLifecycleBegin	Contains information that is passed via the QueryLifecycle interface.
QueryLifecycleBeginBase	Query begin lifecycle base class implementation for causal algorithms.
QueryLifecycleEnd	Contains information that is passed via the QueryLifecycle interface.
QueryLifecycleEndBase	Query end lifecycle base class implementation for causal algorithms.
QueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
QuerySamplingOptions	Interface for approximate sampling inference algorithms, which can be implemented in addition to QueryOptions.
R2CrossValidationTestResult	Represents the R Squared statistic (Coefficient of determination) on a partition of data.
RandomDefault	Default random number generator, that is consistent across the different APIs.
RandomNumberGenerator	Interface for random number generation.
ReaderOptions	Options that apply to the reading of non temporal data.
ReadInfo	Provides information about a non temporal record.
ReadOptions	Provides information to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions).
RegressionStatistics	Calculates statistics for a network which is used to predict continuous values (regression).
RelevanceTreeInference	An exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.
RelevanceTreeInferenceFactory	Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
RelevanceTreeQueryLifecycleBegin	Query lifecycle begin implementation for the Relevance Tree algorithm.
RelevanceTreeQueryLifecycleEnd	Query end lifecycle implementation for the Relevance Tree algorithm.
RelevanceTreeQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
RelevanceTreeQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
ScoreMethod	The scoring mechanism used to evaluate different Bayesian network structures during a search.
SearchLinkOutput	Contains information about a new link learnt using the com.bayesserver.learning.structure.search.SearchStructuralLearning algorithm.
SearchStructuralLearning	A structural learning algorithm for Bayesian networks based on Search and Score.
SearchStructuralLearningOptions	Options for structural learning with the com.bayesserver.learning.structure.search.SearchStructuralLearning class.
SearchStructuralLearningOutput	Contains information returned from the com.bayesserver.learning.structure.search.SearchStructuralLearning algorithm.
SearchStructuralLearningProgressInfo	Progress information returned from the Search based structural learning algorithm.
SensitivityFunctionOneWay	Represents the result on a one-way sensitivity to parameters analysis.
SensitivityFunctionTwoWay	Represents the result on a two-way sensitivity to parameters analysis.
SensitivityToParameters	Calculates the affect of one or more parameters on the value of a hypothesis.
SepsetDefinition	The definition of a sepset in a junction tree, without the instantiation of the distribution.
SoftEvidence	Helper methods for manipulating soft/virtual evidence.
SortOrder	The sort order of states for new discrete variables.
State	Represents a state of a variable.
StateCollection	Represents a collection of states belonging to a Variable.
StateContext	Identifies a State and contextual information such as the time (zero based).
StateNotFoundAction	Determines the action to take when a state name or value cannot be matched to a variable state.
StateValueType	The type of value represented by a State.
Stop	Interface to allow early completion of a long running task.
StructuralLearning	Defines methods for learning the structure (links) of a Bayesian network.
StructuralLearningOptions	Options governing a structural learning algorithm.
StructuralLearningOutput	Contains information returned from a structural learning algorithm.
StructuralLearningProgress	Interface to provide progress information during structural learning.
StructuralLearningProgressInfo	Interface to provide progress information during structural learning.
Table	Used to represent probability distributions, conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
Table.MarginalizeLowMemoryOptions	Options controlling Table.marginalizeLowMemory(com.bayesserver.Table[]).
Table.MaxValue
Table.NonZeroValues	Used to report non zero table values.
TableAccessor	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().
TableExpression	Represents an expression that is used to generate Table distributions.
TableExpressionNormalization	The type of normalization to apply to a table (if any) once an expression has generated the values.
TableIterator	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().
TANLinkOutput	Contains information about a new link learnt using the com.bayesserver.learning.structure.tan.TANStructuralLearning algorithm.
TANStructuralLearning	A structural learning algorithm for Bayesian networks based on the Tree augmented naive Bayes (TAN) algorithm.
TANStructuralLearningOptions	Options for structural learning with the com.bayesserver.learning.structure.tan.TANStructuralLearning class.
TANStructuralLearningOutput	Contains information returned from the com.bayesserver.learning.structure.tan.TANStructuralLearning algorithm.
TANStructuralLearningProgressInfo	Progress information returned from the TAN structural learning algorithm.
TemporalReaderOptions	Options that apply to the reading of temporal data.
TemporalReadInfo	Provides information about a temporal record.
TemporalType	The node type for networks that include temporal/sequential support.
TimeSeriesMode	Determines how time series distributions are learned.
TimeValueType	The type of values stored in a time column.
TopologicalSort	Contains methods to sort nodes in a Bayesian network in topological order.
TopologicalSortNodeInfo	Information about the topological order of a node.
TreeQuery	Contains methods to determine properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
TreeQueryOptions	Options which affect the calculation performed by a TreeQuery.
TreeQueryOutput	Contains information output by a TreeQuery.
Unroller	Unrolls a Dynamic Bayesian network into the equivalent Bayesian network.
UnrollOptions	Options governing the unrolling of a Dynamic Bayesian network.
UnrollOutput	Contains information returned by Unroller.unroll(com.bayesserver.Network, int, com.bayesserver.UnrollOptions).
UnrollOutput.NodeTime	Identifies a node and related time.
UnrollOutput.VariableTime	Identifies a variable and related time.
Validation	Methods to test whether adjustment inputs are valid.
ValidationException	Raised by an identification algorithm when validation fails.
ValidationOptions	Options for classes that implement Validation
ValidationOptions	Represents options that govern the validation of a network.
ValueOfInformation	Contains methods to determine what new evidence is most likely to reduce the uncertainty of a variable.
ValueOfInformationKind	The type of value of information statistic calculated.
ValueOfInformationOptions	Options for calculating ValueOfInformation.
ValueOfInformationOutput	Contains the results of the tests carried out using ValueOfInformation.
ValueOfInformationTestOutput	Contains information about a variable tested via ValueOfInformation.
Variable	Represents a discrete or continuous random variable.
VariableContext	Represents a variable and associated information such as time, and whether it is marked as head or tail.
VariableContextCollection	Represents a read-only collection of variables.
VariableDefinition	Defines how a variable should be created.
VariableEliminationInference	An exact inference algorithm for Bayesian networks and Dynamic Bayesian networks, loosely based on the Variable Elimination algorithm.
VariableEliminationInferenceFactory	Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
VariableEliminationQueryLifecycleBegin	Query lifecycle begin implementation for the Variable Elimination algorithm.
VariableEliminationQueryLifecycleEnd	Query end lifecycle implementation for the Variable Elimination algorithm.
VariableEliminationQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
VariableEliminationQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
VariableGenerator	Generates variables from a data source.
VariableGeneratorOptions	Options that affect the generation of variables from data.
VariableGeneratorProgress	Interface to provide progress information during data discovery (VariableGenerator).
VariableGeneratorProgressInfo	Interface to provide progress information during data discovery (VariableGenerator).
VariableInfo	Contains the generated Variable and any supplementary information.
VariableInfoCount	Reports weighted and unweighted record counts.
VariableInfoCounts	Reports counts for each variable.
VariableInfoValue	Reports general weighted and unweighted information/statistics about a variable.
VariableKind	The kind of variable, such as Probability, Decision or Utility.
VariableMap	Maps between a custom variable order and the default sorted variable order.
VariableReference	Identifies a Variable and data binding information.
VariableValueType	The type of data represented by a Variable.
WeightedValue	A value (which can be null) and its associated weight (support).
WindowDataReader	A data reader that reads windows of data over another data reader.
WindowDataReaderCommand	A data reader command that reads windows of data over another data reader.
WindowDataReaderOptions	Options for creating windowed data readers.
WindowOptions	Options for creating windows over time series data.
WriteStreamAction	Provides an output stream that can be written to.