Class and Description |
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Bounds
Stores the position and size of an element.
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Cancellation
Interface for cancelling long running operations.
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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.
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Expression
Base interface for expressions.
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ExpressionDistribution
Determines what happens when an expression is set on a node distribution.
|
ExpressionReturnType
The type of value returned from an expression.
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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.
|
Class and 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.
|
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.
|
Variable
Represents a discrete or continuous random variable.
|
Class and Description |
---|
Network
Represents a Bayesian Network, or a Dynamic Bayesian Network.
|
Variable
Represents a discrete or continuous random variable.
|
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 . |
Class and 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.
|
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.
|
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.
|
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.
|
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.
|
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.
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