Namespaces

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.

Provides interfaces/classes for handling data.

Contains classes and interfaces for generating variables from data.

Contains classes and interfaces for discretizing continuous data.

Contains classes for sampling data from Bayesian networks and Dynamic Bayesian networks. See TakeSample(IEvidence, Random, DataSamplingOptions) for sample code.

Contains interfaces for performing probabilistic inference with a Bayesian network, such as calculating posterior probabilities and log-likelihoods. See the RelevanceTreeInference class for an algorithm that implements the necessary interfaces.

Provides an exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.

Provides an exact probabilistic inference algorithm for Bayesian networks, loosely based on the Variable Elimination algorithm.

Provides parameter learning for Bayesian networks and Dynamic Bayesian networks. See Learn(IEvidenceReader, ParameterLearningOptions) for sample code.