20 min listen
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
ratings:
Length:
34 minutes
Released:
Jul 17, 2017
Format:
Podcast 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.
Released:
Jul 17, 2017
Format:
Podcast episode
Titles in the series (85)
LM101-003: How to Represent Knowledge using Logical Rules: Episode Summary: In this episode we will learn how to use .rules. to represent knowledge. We discuss how this works in practice and we explain how these ideas are implemented in a special architecture called the production system. by Learning Machines 101