Package com.bayesserver

Interface Summary

Interface	Description
Cancellation	Interface for cancelling long running operations.
Distributer<T>
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.
MultipleIterator.Combination
NameValuesReader	Interface for reading name/value pairs.
NameValuesWriter	Interface for writing name/value pairs.
NetworkMonitor	For internal use.
QueryExpression	Base interface for expressions that are evaluated at query time.
RandomNumberGenerator	Interface for random number generation.
Stop	Interface to allow early completion of a long running task.
Table.NonZeroValues	Used to report non zero table values.
WriteStreamAction	Provides an output stream that can be written to.

Class Summary

Class	Description
ArcReversal	Contains methods to reverse the direction of a Link, known as arc reversal.
Bounds	Stores the position and size of an element.
CLGaussian	Represents a Conditional Linear Gaussian probability distribution.
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).
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.
FunctionVariableExpression	An expression that can be used in a function node/variable.
Interval<T extends Comparable>	An interval, defined by a minimum and maximum with respective open or closed endpoints.
License	Provides license validation.
Link	Represents a directed link in a Bayesian network.
MultipleIterator	Provides methods to iterate over multiple distributions.
Network	Represents a Bayesian Network, or a Dynamic Bayesian Network.
NetworkLinkCollection	Represents the collection of directed links maintained by the Network class.
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.
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
ParameterCounter	Contains methods to determine the number of parameters in a Bayesian network or distribution.
ParameterCountOptions	Options for ParameterCounter.
RandomDefault	Default random number generator, that is consistent across the different APIs.
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).
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
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.
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().
TopologicalSort	Contains methods to sort nodes in a Bayesian network in topological order.
TopologicalSortNodeInfo	Information about the topological order of a node.
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.
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.
VariableMap	Maps between a custom variable order and the default sorted variable order.

Enum Summary

Enum	Description
CausalObservability	Gets or sets the observability of a node which is causal.
CollectionAction	Specifies how the collection is changed.
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.
NodeDistributionKind	The kind of distribution, such as a standard Probability or Experience table.
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.
PropagationMethod	The propagation method used during inference.
StateValueType	The type of value represented by a State.
TableExpressionNormalization	The type of normalization to apply to a table (if any) once an expression has generated the values.
TemporalType	The node type for networks that include temporal/sequential support.
VariableKind	The kind of variable, such as Probability, Decision or Utility.
VariableValueType	The type of data represented by a Variable.

Exception Summary

Exception	Description
ExpressionException	Exception raised during lexing, parsing or evaluation of an expression.
InvalidNetworkException	Raised when a network has not been correctly specified.
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.