Probabilistic Models for Inference and Estimation

4.0 credits

Probability and stochastic processes, IID and Markov processes, basicsof inference and estimation, MAP and ML estimates, modeling withlatent variables, expectation maximization, hidden Markov Models,stochastic context free grammars, Markov and conditional randomfields, energy models. Select applications in computer vision, machinelearning, image processing, communications, and bioinformatics.