LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
Jul 17, 2017•34 min•Season 1Ep. 66
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Episode description
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.
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