Description
This is the primary textbook on pattern recognition to provide the Bayesian perspective. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers don’t seem to be feasible. It uses graphical models to explain probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and a few experience in the usage of probabilities would be helpful despite the fact that not very important as the book features a self-contained introduction to basic probability theory.