Probabilistic Inference and Machine Learning

Covers the fundamental principles of probabilistic inference and computational learning systems. Topics include Bayes decision and utility theory, Monte Carlo and Markov chain Monte Carlo methods; learning with complete data; Bayesian networks, Markov random fields and factor graphs; models; learning with incomplete data; computational learning and PAC learning theory.