Uses of Package
com.bayesserver

Packages that use com.bayesserver

Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.causal
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	Description
Bounds	Stores the position and size of an element.
Cancellation	Interface for cancelling long running operations.
CausalObservability	Gets or sets the observability of a node which is causal.
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.
ExpressionDistribution	Determines what happens when an expression is set on a node distribution.
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.
RandomNumberGenerator	Interface for random number generation.
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	Description
Cancellation	Interface for cancelling long running operations.
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.causal

Class	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.
Variable	Represents a discrete or continuous random variable.

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

Class	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	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	Description
Network	Represents a Bayesian Network, or a Dynamic Bayesian Network.
RandomNumberGenerator	Interface for random number generation.
Variable	Represents a discrete or continuous random variable.

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

Class	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.
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.learning.parameters

Class	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	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	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	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.