swMATH ID: 8127
Software Authors: Kevin Murphy
Description: Software packages for graphical models/Bayesian networks. Graphical models (GMs) are a way to represent conditional independence assumptions by using graphs. Specifically, nodes represent random variables and lack of edges represent conditional independencies. The graph is a useful visual representation of complex stochastic systems. The graphical structure is also the basis of efficient inference algorithms.There are many different kinds of graphical models, but the two most popular ones are based on directed acylic graphs (also called “Bayesian networks”) and on undirected graphs (also called “Markov random fields”). In this article, we review some of the more popular and/or recent software packages for dealing with graphical models. A more extensive comparison can be found at http://www.cs.ubc.ca/ murphyk/Software/bnsoft.html.
Homepage: http://www.cs.ubc.ca/~murphyk/Software/bnsoft.html
Related Software: Blaise; R; Infer.NET; SemiPar; KernSmooth; PRMLT; NUTS; Stan; BUGS; densEstBayes; JAGS; RStan; BayesDA; rstan; HUGIN; MIM; Graph_sampler; VIBES; bnlearn; deal
Cited in: 8 Documents

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