## 简介

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.

## 目录

Mathematical notation

Contents

1 Introduction

2 Probability Distributions

3 Linear Models for Regression

4 Linear Models for Classification

5 Neural Networks

6 Kernel Methods

7 Sparse Kernel Machines

8 Graphical Models

9 Mixture Models and EM

10 Approximate Inference

11 Sampling Methods

12 Continuous Latent Variables

13 Sequential Data

14 Combining Models

Appendix A Data Sets

Appendix B Probability Distributions

Appendix C Properties of Matrices

Appendix D Calculus of Variations

Appendix E Lagrange Multipliers

References

Index