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BayesServer Namespace

Contains classes and interfaces for defining the structure and distributions of a Bayesian network, and to save and load them. To perform inference, such as calculating posterior probabilities and log-likelihoods see the BayesServer.Inference namespace.
Classes
  ClassDescription
Public classArcReversal
Contains methods to reverse the direction of a Link, known as arc reversal.
Public classCancellation
Class for canceling long running operations.
Public classCLGaussian
Represents a Conditional Linear Gaussian probability distribution.
Public classCustomProperty
Stores a custom property.
Public classCustomPropertyCollection
Stores custom properties for a variety of objects.
Public classDag
Includes methods for testing whether a network is a Directed Acyclic Graph (DAG).
Public classDecomposeOptions
Options used by the Decomposer class.
Public classDecomposeOutput
Contains information returned by Decompose.
Public classDecomposer
Contains methods to decompose nodes with multiple variables into their single variable equivalents.
Public classInvalidNetworkException
Raised when a network has not been correctly specified.
Public classLicense
Provides license validation.
Public classLink
Represents a directed link in a Bayesian network.
Public classMultipleIterator
Provides methods to iterate over multiple distributions.
Public classNetwork
Represents a Bayesian Network, or a Dynamic Bayesian Network. To perform inference with a network see the BayesServer.Inference namespace.
Public classNetworkLinkCollection
Represents the collection of directed links maintained by the Network class. See Links . Duplicates and null values are not allowed.
Public classNetworkNodeCollection
Represents the collection of Nodes maintained by the Network class. Duplicates and null values are not allowed.
Public classNetworkNodeGroupCollection
A collection of groups.
Public classNetworkVariableCollection
Represents a read-only collection of variables that belong to a network. When a variable is added to a Node it is automatically inserted into this collection.
Public classNode
Represents a node with one or more variables in a Bayesian network.
Public classNodeDistributionChangedEventArgs
Contains information passed by DistributionChanged.
Public classNodeDistributionOptions
Options that apply to all distributions of a particular node.
Public classNodeDistributions
Represents the distributions assigned to a Node. Temporal nodes may require more than one distribution to be fully specified.
Public classNodeGroup
Allows nodes to be assigned to one or more groups.
Public classNodeGroupCollection
Represents the collection of groups a node belongs to.
Public classNodeLinkCollection
Represents a read-only collection of links. To add a link to a network see Links.
Public classNodeVariableCollection
Represents the collection of variables belonging to a .
Public classNotInDomainException
Raised when the arguments to a mathematic function are not in the domain of the function (undefined).
Public classNotSpdException
Raised when a matrix is not positive definite.
Public classParameterCounter
Contains methods to determine the number of parameters in a Bayesian network or distribution.
Public classParameterCountOptions
Options for ParameterCounter.
Public classState
Represents a state of a variable. E.g. the discrete variable Gender might have two states, Male and Female.
Public classStateCollection
Represents a collection of states belonging to a Variable.
Public classTable
Used to represent conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
Public classTableAccessor
Allows random access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in SortedVariables.
Public classTableIterator
Allows sequential access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in SortedVariables.
Public classTopologicalSort
Contains methods to sort nodes in a Bayesian network in topological order.
Public classUnroller
Unrolls a Dynamic Bayesian network into the equivalent Bayesian network.
Public classUnrollOptions
Options governing the unrolling of a Dynamic Bayesian network.
Public classUnrollOutput
Contains information returned by Unroll.
Public classVariable
Represents a discrete or continuous random variable.
Public classVariableContext
Represents a variable and associated information such as time, and whether it is marked as head or tail.
Public classVariableContextCollection
Represents a read-only collection of variables.
Public classVariableMap
Maps between a custom variable order and the default sorted variable order.
Structures
  StructureDescription
Public structureBounds
Stores the position and size of an element.
Public structureIntervalT
An interval, defined by a minimum and maximum with respective open or closed endpoints.
Public structureNodeDistributionKey
Identifies a distribution assigned or to be assigned to a node.
Public structureStateContext
Identifies a State and contextual information such as the time (zero based).
Public structureTableMarginalizeLowMemoryOptions
Options controlling MarginalizeLowMemory.
Public structureValidationOptions
Represents options that govern the validation of a network. See Validate.
Interfaces
  InterfaceDescription
Public interfaceICancellation
Interface for cancelling long running operations.
Public interfaceIDistribution
Interface specifying the required methods and properties for a probability distribution. For example the Table class implements this interface.
Public interfaceIStop
Interface to allow early completion of a long running task.
Delegates
  DelegateDescription
Public delegateMultipleIteratorCombination
Called by the Iterate methods to indicate a new iteration / combination.
Enumerations
  EnumerationDescription
Public enumerationHeadTail
Indicates whether a variable is marked as head or tail in a distribution. See VariableContext.
Public enumerationIntervalEndPoint
The type of end point for an interval.
Public enumerationNodeDistributionKind
The kind of distribution, such as a standard Probability or Experience table.
Public enumerationNoisyOrder
Determines the order in which the states of a parent of a noisy node increasingly affect the noisy states.
Public enumerationNoisyType
Identifies the noisy node type, if any.
Public enumerationPropagationMethod
The propagation method used during inference.
Public enumerationStateValueType
The type of value represented by a State.
Public enumerationTemporalType
The node type for networks that include temporal/sequential support. I.e. Dynamic Bayesian Networks (DBN).
Public enumerationVariableKind
The kind of variable, such as Probability, Decision or Utility.
Public enumerationVariableValueType
The type of data represented by a Variable.