Bayes Server .NET API

The Bayes Server Class Library is a .NET library for Bayesian networks and Dynamic Bayesian networks.

For code samples, please visit the Bayes Server code center.

Namespaces

BayesServer

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.

BayesServer.Inference

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.

BayesServer.Analysis

Contains classes for performing analysis tasks that require inference and/or data, such as calculating value of information.

BayesServer.Optimization

Contains classes for optimizing evidence to minimize/maximize/seek a function value, continuous variable or discrete state.

BayesServer.Causal

Contains classes for Causal inference using Bayesian networks.

BayesServer.Data

Provides interfaces/classes for handling data.

BayesServer.Data.Discovery

Contains classes and interfaces for generating variables from data.

BayesServer.Data.Distributed

Contains classes and interfaces for distributed data driven processes such as distributed parameter learning.

BayesServer.Data.Sampling

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

BayesServer.Data.TimeSeries

Contains classes and interfaces for manipulating time series data.

BayesServer.Distributed

Contains classes and interfaces for distributed algorithms such as distributed parameter learning.

BayesServer.Inference.Approximate

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

BayesServer.Learning.Parameters

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

BayesServer.Learning.Structure

Contains classes for learning the structure (links) of a Bayesian network or Dynamic Bayesian network.

BayesServer.Statistics

Contains interfaces and classes for performing statistical/probabilistic tests.