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BayesNetBP (version 1.6.1)

Bayesian Network Belief Propagation

Description

Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks . For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks . The optional 'cyjShiny' package for running the Shiny app is available at . Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.

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Version

Install

install.packages('BayesNetBP')

Monthly Downloads

310

Version

1.6.1

License

GPL (>= 2)

Maintainer

Han Yu

Last Published

May 8th, 2022

Functions in BayesNetBP (1.6.1)

ElimTreeInitialize

Initialize the elimination tree

Convert a bn object to graphNEL object

ClusterTree-class

An S4 class of the cluster tree.

Saccharomyces Cerevisiae eQTL data from Kruglak et. al. (2005)

SummaryMarginals

Summary a continuous marginal distribution

ClusterTreeCompile

Compile the cluster tree

LocalModelCompile

Model compilation

Possible values of a discrete variable

A simulated data from the Chest Clinic example

Sampling from the Bayesian network

A simulated data from the Emission example

A ClusterTree Example of Emission Model

Initialize a ClusterTree object

qtlnet_to_graphNEL

Convert qtlnet to graphNEL object

Plot the cluster tree

Mus Musculus HDL QTL data from Leduc et. al. (2012)

Plot the marginal distributions

Plot the Bayesian network

Propagate the cluster tree

A ClusterTree Example of Liver Model

Launch the BayesNetBP Shiny App

Compute signed and symmetric Kullback-Leibler divergence

Obtain marginal distributions

Queries of discrete variable distributions

Absorb evidence into the model