31 min listen
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
ratings:
Length:
35 minutes
Released:
Nov 9, 2015
Format:
Podcast episode
Description
In this episode 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. Concepts of Markov Random Fields and Monte Carlo Markov Chain methods are discussed. For additional details and technical notes, please visit the website: www.learningmachines101.com
Also feel free to visit us at twitter: @lm101talk
Also feel free to visit us at twitter: @lm101talk
Released:
Nov 9, 2015
Format:
Podcast episode
Titles in the series (85)
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