Uses of Package
com.bayesserver

Packages that use com.bayesserver

Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.data
com.bayesserver.data.discovery
com.bayesserver.data.sampling
com.bayesserver.inference
com.bayesserver.learning.parameters
com.bayesserver.learning.structure
com.bayesserver.optimization
com.bayesserver.statistics

Classes in com.bayesserver used by com.bayesserver

Class and Description
Bounds Stores the position and size of an element.
Cancellation Interface for cancelling long running operations.
CLGaussian Represents a Conditional Linear Gaussian probability distribution.
CollectionAction Specifies how the collection is changed.
CustomProperty Stores a custom property.
CustomPropertyCollection Stores custom properties for a variety of objects.
DecomposeOptions Options used by the Decomposer class.
DecomposeOutput Contains information returned by Decomposer.decompose(com.bayesserver.Network, com.bayesserver.DecomposeOptions).
Distribution Interface specifying the required methods and properties for a probability distribution.
DistributionExpression Base interface for expressions that generate distributions.
Expression Base interface for expressions.
ExpressionReturnType The type of value returned from an expression.
HeadTail Indicates whether a variable is marked as head or tail in a distribution.
IntervalEndPoint The type of end point for an interval.
Link Represents a directed link in a Bayesian network.
MultipleIterator.Combination
NameValuesReader Interface for reading name/value pairs.
NameValuesWriter Interface for writing name/value pairs.
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.
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.
ParameterCountOptions Options for ParameterCounter.
PropagationMethod The propagation method used during inference.
QueryExpression Base interface for expressions that are evaluated at query time.
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).
StateValueType The type of value represented by a State.
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.
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().
TemporalType The node type for networks that include temporal/sequential support.
TopologicalSortNodeInfo Information about the topological order of a node.
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.
ValidationOptions Represents options that govern the validation of a network.
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.
VariableKind The kind of variable, such as Probability, Decision or Utility.
VariableValueType The type of data represented by a Variable.
WriteStreamAction Provides an output stream that can be written to.

Classes in com.bayesserver used by com.bayesserver.analysis

Class and Description
CLGaussian Represents a Conditional Linear Gaussian probability distribution.
Distribution Interface specifying the required methods and properties for a probability distribution.
Interval An interval, defined by a minimum and maximum with respective open or closed endpoints.
Network Represents a Bayesian Network, or a Dynamic Bayesian Network.
Node Represents a node with one or more variables in a Bayesian network.
NodeDistributionKey Identifies a distribution assigned or to be assigned to a node.
State Represents a state of a variable.
StateContext Identifies a State and contextual information such as the time (zero based).
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.

Classes in com.bayesserver used by com.bayesserver.data

Class and Description
Network Represents a Bayesian Network, or a Dynamic Bayesian Network.
Variable Represents a discrete or continuous random variable.

Classes in com.bayesserver used by com.bayesserver.data.discovery

Class and Description
Cancellation Interface for cancelling long running operations.
Interval An interval, defined by a minimum and maximum with respective open or closed endpoints.
StateValueType The type of value represented by a State.
Variable Represents a discrete or continuous random variable.
VariableKind The kind of variable, such as Probability, Decision or Utility.
VariableValueType The type of data represented by a Variable.

Classes in com.bayesserver used by com.bayesserver.data.sampling

Class and Description
Network Represents a Bayesian Network, or a Dynamic Bayesian Network.
Variable Represents a discrete or continuous random variable.

Classes in com.bayesserver used by com.bayesserver.inference

Class and Description
Cancellation Interface for cancelling long running operations.
Distribution Interface specifying the required methods and properties for a probability distribution.
Network Represents a Bayesian Network, or a Dynamic Bayesian Network.
Node Represents a node with one or more variables in a Bayesian network.
PropagationMethod The propagation method used during inference.
State Represents a state of a variable.
Table Used to represent probability distributions, conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
Variable Represents a discrete or continuous random variable.

Classes in com.bayesserver used by com.bayesserver.learning.parameters

Class and Description
Cancellation Interface for cancelling long running operations.
Distributer
Distribution Interface specifying the required methods and properties for a probability distribution.
NameValuesReader Interface for reading name/value pairs.
NameValuesWriter Interface for writing name/value pairs.
Network Represents a Bayesian Network, or a Dynamic Bayesian Network.
Node Represents a node with one or more variables in a Bayesian network.
NodeDistributionKey Identifies a distribution assigned or to be assigned to a node.
Stop Interface to allow early completion of a long running task.

Classes in com.bayesserver used by com.bayesserver.learning.structure

Class and Description
Cancellation Interface for cancelling long running operations.
Link Represents a directed link in a Bayesian network.
Node Represents a node with one or more variables in a Bayesian network.
Stop Interface to allow early completion of a long running task.
Variable Represents a discrete or continuous random variable.

Classes in com.bayesserver used by com.bayesserver.optimization

Class and Description
Cancellation Interface for cancelling long running operations.
Network Represents a Bayesian Network, or a Dynamic Bayesian Network.
State Represents a state of a variable.
Stop Interface to allow early completion of a long running task.
Variable Represents a discrete or continuous random variable.

Classes in com.bayesserver used by com.bayesserver.statistics

Class and Description
CLGaussian Represents a Conditional Linear Gaussian probability distribution.
Distribution Interface specifying the required methods and properties for a probability distribution.
Table Used to represent probability distributions, conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
VariableContext Represents a variable and associated information such as time, and whether it is marked as head or tail.