Probabilistic Graphical Models 2: Inference

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• Learning Outcomes: By the end of this course, you will be able to take a given PGM and

  Execute the basic steps of a variable elimination or message passing algorithm

  Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible

  Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings

  Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective

  Design Metropolis Hastings proposal distributions that are more likely to give good results

  Compute a MAP assignment by exact inference

  Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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