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Bayes Server .NET Library Help

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
NamespaceDescription
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.Analysis
Contains classes for performing analysis tasks that require inference and/or data, such as calculating value of information.
BayesServer.Data
Provides interfaces/classes for handling data.
BayesServer.Data.Discovery
Contains classes and interfaces for generating variables from data.
BayesServer.Data.Discovery.Discretization
Contains classes and interfaces for discretizing continuous 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
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.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.Inference.Approximate.LikelihoodSampling
Contains classes for performing inference with Bayesian networks and Dynamic Bayesian networks using an approximate inference method called Likelihood sampling.
BayesServer.Inference.Approximate.LoopyBelief
Contains classes for performing inference with Bayesian networks and Dynamic Bayesian networks using an approximate inference method based on loopy belief propagation.
BayesServer.Inference.RelevanceTree
Provides an exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.
BayesServer.Inference.VariableElimination
Provides an exact probabilistic inference algorithm for Bayesian networks, loosely based on the Variable Elimination algorithm.
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.Learning.Structure.ChowLiu
Contains classes for learning the structure (links) of a Bayesian network or Dynamic Bayesian network using the Chow-Liu algorithm.
BayesServer.Learning.Structure.Features
Contains classes to perform feature selection.
BayesServer.Learning.Structure.Hierarchical
BayesServer.Learning.Structure.Independence
Contains classes concerned with independence/conditional independence testing.
BayesServer.Learning.Structure.PC
Contains classes for learning the structure (links) of a Bayesian network or Dynamic Bayesian network using the PC algorithm.
BayesServer.Learning.Structure.Search
BayesServer.Learning.Structure.TAN
Contains classes for learning the structure (links) of a Bayesian network or Dynamic Bayesian network using the Tree Augmented Naive Bayes algorithm.
BayesServer.Statistics
Contains interfaces and classes for performing statistical/probabilistic tests.