Graph-Based Perspective on Variable Elimination

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Skills You'll Learn

Inference, Gibbs Sampling, Markov Chain Monte Carlo (MCMC), Belief Propagation

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4.6 (448 ratings)
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LL

Mar 12, 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.

YP

May 29, 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

From the lesson
Variable Elimination
This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure.

Taught By

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    Daphne Koller

    Professor

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