LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)

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. Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

Episode metadata supplied by the publisher feed · Published Jan 26, 2015

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LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)

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