Description
Book SynopsisNonlinear Bayesian modelling is a relatively new field, but one that has seen a recent explosion of interest. Nonlinear models offer more flexibility than those with linear assumptions, and their implementation has now become much easier due to increases in computational power. Bayesian methods allow for the incorporation of prior information, allowing the user to make coherent inference. Bayesian Methods for Nonlinear Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian methods.
* 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.
* Discuss
Trade Review"The exercises and the excellent presentation style make this book qualified t be a textbook in a graduate level nonlinear regression course." (
Journal of Statistical Computation and Simulation, July 2005)
"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)
Table of ContentsPreface
Acknowledgements.
Introduction
Bayesian Modelling
Curve Fitting
Surface Fitting
Classification using Generalised Nonlinear Models
Bayesian Tree Models
Partition Models
Nearest-Neighbour Models
Multiple Response Models
Appendix A: Probability Distributions
Appendix B: Inferential Processes
References
Index
Author Index
