Topics include: (i) The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities; (ii) reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods); (iii) learning methods for both parameters and structure in a PGM; (iv) using a PGM for decision making under uncertainty.

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