Bayesian networks, Markov random fields, factor graphs, Markov properties, standard models as graphical models, graph theory (e.g., moralization and triangulation), probabilistic inference (including pearl’s belief propagation, Hugin, and Shafer-Shenoy), junction threes, dynamic Bayesian networks (including hidden Markov models), learning new models, models in practice.
