LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain) - a podcast by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.

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!

Further episodes of Learning Machines 101

Further podcasts by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.

Website of Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.