LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
Learning Machines 101 - A podcast by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
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In this episode of Learning Machines 101 (www.learningmachines101.com) we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values of the unobservable variables given the observable variables. Specifically, Monte Carlo Markov Chain ( MCMC ) methods are discussed.
