Fall. 4 credits. Prerequisite: probability theory (BTRY 3080 or equivalent), programming and data structures (CS 2110 or equivalent). Recommended prerequisite: course in statistical methods (BTRY 4090 or equivalent). Staff.A thorough introduction to probabilistic graphical models, a flexible and powerful graph-based framework for probabilistic modeling. Covers directed and undirected models, exact and approximate inference, and learning in the presence of latent variables. Hidden Markov models, conditional random fields, and Kalman filtering are explored in detail.