Difference between revisions of "Belief Propagation"
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| − | This is a [[category::Method]] proposed | + | This is a [[category::Method]] proposed by [[RelatedPaper::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). The basic idea is to compute the marginal distribution of unobserved nodes, based on the conditional distribution of observed nodes. | + | Belief Propagation is a message passing inference method for statistical graphical models (e.g. Markov random fields). The basic idea is to compute the marginal distribution of unobserved nodes, based on the conditional distribution of observed nodes. When the graphical model is both a factor graph and a tree, the exact marginals can be obtained. |
== Definition == | == Definition == | ||
== Inference == | == Inference == | ||
Revision as of 23:21, 26 September 2011
This is a Method proposed by 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). The basic idea is to compute the marginal distribution of unobserved nodes, based on the conditional distribution of observed nodes. When the graphical model is both a factor graph and a tree, the exact marginals can be obtained.
