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Unformatted text preview: CSci 5512: Gibbs Sampling for Approximate Inference in Bayesian Networks Let p ( X 1 , . . . , X n  e 1 , . . . , e m ) denote the joint distribution of a set of random variables ( X 1 , . . . , X n ) conditioned on a set of evidence variables ( e 1 , . . . , e m ). Gibbs sampling is an algorithm to generate a sequence of samples from such a joint probability distribution. The purpose of such a sequence is to approximate the joint distribution (as with a histogram), or to compute an integral (such as an expected value). Gibbs sampling is applicable when the joint distribution is not known explicitly, but the con ditional distribution of each variable is known. The Gibbs sampling algorithm is used to generate an instance from the distribution of each variable in turn, conditional on the current values of the other variables. It can be shown that the sequence of samples comprises a Markov chain, and the stationary distribution of that Markov chain is just the soughtafter joint distribution. Gibbs sampling is particularly welladapted to sampling the posterior distribution of a Bayesian network, since Bayesian networks are typically specified as a collection of conditional distributions.since Bayesian networks are typically specified as a collection of conditional distributions....
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This note was uploaded on 02/07/2012 for the course CSCI 5512 taught by Professor Staff during the Spring '08 term at Minnesota.
 Spring '08
 Staff

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