Pattern Recognition and Machine Learning / Edition 1

Pattern Recognition and Machine Learning / Edition 1

by Christopher Bishop
     
 

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

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Overview

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

Product Details

ISBN-13:
9780387310732
Publisher:
Springer New York
Publication date:
04/06/2011
Series:
Information Science and Statistics Series
Edition description:
1st ed. 2006. Corr. 2nd printing 2011
Pages:
740
Sales rank:
191,844
Product dimensions:
7.20(w) x 9.40(h) x 1.70(d)

Related Subjects

Table of Contents

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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