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6.438 Algorithms for Inference

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Prereq: 18.06 and (6.008, 6.041B, or 6.436)
Units: 4-0-8
Lecture: TR9.30-11 (32-123) Recitation: F10 (1-190) or F11 (1-190) +final

Introduction to statistical inference with probabilistic graphical models. Directed and undirected graphical models, and factor graphs, over discrete and Gaussian distributions; hidden Markov models, linear dynamical systems. Sum-product and junction tree algorithms; forward-backward algorithm, Kalman filtering and smoothing. Min-sum and Viterbi algorithms. Variational methods, mean-field theory, and loopy belief propagation. Particle methods and filtering. Building graphical models from data, including parameter estimation and structure learning; Baum-Welch and Chow-Liu algorithms. Selected special topics.
G. Wornell
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