Bayesian Methods for Nonlinear Classification and Regression
- Focuses on the problems of classification and regression using flexible, data-driven approaches.
- Demonstrates how Bayesian ideas can be used to improve existing statistical methods.
- Includes coverage of Bayesian additive models, decision trees, nearest-neighbour, wavelets, regression splines, and neural networks.
- Emphasis is placed on sound implementation of nonlinear models.
- Discusses medical, spatial, and economic applications.
- Includes problems at the end of most of the chapters.
- Supported by a web site featuring implementation code and data sets.
The material available at the link below is 'Matlab code for implementing the examples in the book'.
Classification using Generalised Nonlinear Models.
Bayesian Tree Models.
Multiple Response Models.
Appendix A: Probability Distributions.
Appendix B: Inferential Processes.
"Its in-depth coverage of implementation issues and detailed discussion of pros and cons of different modeling strategies make it attractive for many researchers.” (Technometrics, May 2004)
"...a fascinating account of a rapidly evolving area of statistics..." (Short Book Reviews, December 2002)
"...will benefit researchers...also suitable for graduate students..." (Mathematical Reviews, 2003m)