Difference between revisions of "Belief Propagation"
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This is a [[Category::method]] proposed in [[RelatedPaper::Judea Pearl, 1982: Reverend Bayes on inference engines: A distributed hierarchical approach, AAAI 1982]]. | This is a [[Category::method]] proposed in [[RelatedPaper::Judea Pearl, 1982: Reverend Bayes on inference engines: A distributed hierarchical approach, AAAI 1982]]. | ||
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| + | Belief Propagation is a message passing inference method for statistical graphical models (e.g. Markov random fields), especially when the graphical model is both a factor graph and a tree (it can compute exact marginals). The basic idea of belief propagation is to compute the marginal distribution of unobserved nodes, based on the conditional distribution of observed nodes. | ||
== Definition == | == Definition == | ||
== Inference == | == Inference == | ||
Revision as of 16:17, 26 September 2011
This is a method proposed in Judea Pearl, 1982: Reverend Bayes on inference engines: A distributed hierarchical approach, AAAI 1982.
Belief Propagation is a message passing inference method for statistical graphical models (e.g. Markov random fields), especially when the graphical model is both a factor graph and a tree (it can compute exact marginals). The basic idea of belief propagation is to compute the marginal distribution of unobserved nodes, based on the conditional distribution of observed nodes.
