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

	Class	Description
	Cancellation	Class for cancelling long running operations.
	CancelledException	Raised when an operation has been cancelled.
	CLGaussian	Represents a Conditional Linear Gaussian probability distribution.
	InvalidNetworkException	Raised when a network has not been correctly specified.
	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. To perform inference with a network see the BayesServer.Inference namespace.
	NetworkLinkCollection	Represents the collection of directed links maintained by the Network class. See Links . Duplicates and null values are not allowed.
	NetworkNodeCollection	Represents the collection of Nodes maintained by the Network class. Duplicates and null values are not allowed.
	NetworkVariableCollection	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.
	Node	Represents a node with one or more variables in a Bayesian network.
	NodeDistributions	Represents the distributions assigned to a Node. Temporal nodes may require more than one distribution to be fully specified.
	NodeLinkCollection	Represents a read-only collection of links. To add a link to a network see Links.
	NodeVariableCollection	Represents the collection of variables belonging to a BayesServer..::.Node.
	State	Represents a state of a variable. E.g. the discrete variable Gender might have two states, Male and Female.
	StateCollection	Represents a collection of states belonging to a Variable.
	Table	Used to represent conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
	TableAccessor	Allows random access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in SortedVariables.
	TableIterator	Allows sequential access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in SortedVariables.
	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.

Structures

	Structure	Description
	Table..::.MarginalizeLowMemoryOptions	Options controlling MarginalizeLowMemory(array<Table>[]()[], Table..::.MarginalizeLowMemoryOptions).
	ValidationOptions	Represents options that govern the validation of a network. See Validate(ValidationOptions).

Interfaces

	Interface	Description
	ICancellation	Interface for cancelling long running operations.
	IDistribution	Interface specifying the required methods and properties for a probability distribution. For example the Table class implements this interface.

Delegates

	Delegate	Description
	MultipleIterator..::.Combination	Called by the Iterate methods to indicate a new iteration / combination.

Enumerations

	Enumeration	Description
	HeadTail	Indicates whether a variable is marked as head or tail in a distribution. See VariableContext.
	TemporalType	The node type for networks that include temporal/sequential support. I.e. Dynamic Bayesian Networks (DBN).
	VariableValueType	The type of data represented by a Variable.