Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. It provides an accessible introduction to approximate inference algorithms that allow for fast, approximate answers in situations where exact answers are not feasible. With its use of graphical models to describe probability distributions, this book is a great resource for those with no prior knowledge of pattern recognition or machine learning concepts. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful, though not essential as the book includes a self-contained introduction to basic probability theory.