LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
Learning Machines 101 - Un pódcast de Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
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Welcome to the 43rd Episode of Learning Machines 101!
We are currently presenting a subsequence of episodes covering the events of the recent Neural Information Processing Systems Conference. However, this week
will digress with a rerun of Episode 22 which nicely complements our previous discussion of the Monte Carlo Markov Chain Algorithm Tutorial. Specifically, today we
discuss the problem of approaches for learning or equivalently parameter estimation in Monte Carlo Markov Chain algorithms. The topics covered in this episode include: What is the pseudolikelihood method and what are its advantages and disadvantages?
What is Monte Carlo Expectation Maximization? And...as a bonus prize...a mathematical theory of "dreaming"!!! The current plan is to return
to coverage of the Neural Information Processing Systems Conference in 2 weeks on January 25!! Check out: www.learningmachines101.com for more details!
