Interface  Description 

Cancellation 
Interface for cancelling long running operations.

Distributer<T>  
Distribution 
Interface specifying the required methods and properties for a probability distribution.

MultipleIterator.Combination  
NameValuesReader 
Interface for reading name/value pairs.

NameValuesWriter 
Interface for writing name/value pairs.

NetworkMonitor 
For internal use.

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  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.

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 readonly collection of variables that belong to a 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.

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 readonly 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 . 
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() . 
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.

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 readonly collection of variables.

VariableMap 
Maps between a custom variable order and the default sorted variable order.

Enum  Description 

CollectionAction 
Specifies how the collection is changed.

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 . 
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  Description 

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.

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