Pattern Recognition and Machine Learning (Information Science and Statistics)

$39.75 (as of June 8, 2020, 10:17)

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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Description

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.

Additional information

Series

Information Science and Statistics

Hardcover

738 pages

Publisher

Springer (August 17, 2006)

Language

English

ISBN-10

0387310738

ISBN-13

978-0387310732

Product Dimensions

7.2 x 1.5 x 10.2 inches

Shipping Weight

3.8 pounds ()

Customer Reviews

4.4 out of 5 stars338 customer ratings

Author

Christopher M. Bishop

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