Pattern Recognition and Machine Learning

Sample ChapterPattern Recognition and Machine LearningChristopher M. BishopCopyrightc 2002 2006 This is an extract from the book Pattern Recognition and Machine Learning published by Springer (2006).It contains the preface with details about the mathematicalnotation, the complete table of contents of thebook and an unabridged version of chapter 8 on Graphical Models. This document, as well as furtherinformation about the book, is available from: cmbishop/PRMLP refacePattern Recognition has its origins in engineering, whereas Machine Learning grewout of computer science. However, these activities can be viewed as two facets ofthe same field, and together they have undergone substantialdevelopment over thepast ten years. In particular, Bayesian methods have grown from a specialist niche tobecome mainstream, while graphical models have emerged as ageneral frameworkfor describing and applying probabilistic models. Also, the practical applicability ofBayesian methods has been greatly enhanced through the development of a range ofapproximate inference algorithms such as variational Bayes and expectation propa-gation.

little need to dwell on such refinements as whether the end poi nts of an interval are included or not. The M× Midentity matrix (also known as the unit matrix) is denoted IM, which will be abbreviated to I where there is no ambiguity about it dimensionality. It has elements Iij that equal 1 if i= jand 0 if i6= j.

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