This is the first textbook on pattern recognition to present the
Bayesian viewpoint. The book presents approximate inference
algorithms that permit fast approximate answers in situations where
exact answers are not feasible. It uses graphical models to
describe 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 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.
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