Markov random fields, or undirected graphical models, are
graphical representations of probability distributions. Each graph represents
a family of distributions -- the nodes of the graph represent random variables,
the edges encode independence assumptions, and weights over the edges and cliques
specify a particular member of the family.
In this talk, I will give the high-level intuitions behind the wide array of inference techniques for discrete markov random fields.
The problem of inference in markov random fields is, in full generality, the problem of answering queries about the probability distribution represented by the markov random field. Key inference tasks include partition function estimation, event probability estimation, and computing the most probable configuration. The talk will give a high-level picture of these queries, and the methods used to answer these queries.
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Pradeep Ravikumar Last modified: Sun Feb 11 11:56:02 EST 2007