Bayesian Methods for Nonlinear Classification and Regression

Nonlinear 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.
* 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.
Primarily of interest to researchers of nonlinear statistical modelling, the book will also be suitable for graduate students of statistics. The book will benefit researchers involved inregression and classification modelling from electrical engineering, economics, machine learning and computer science.Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht. Preface

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 "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)

David G. T. Denison and Christopher C. Holmes are the authors of Bayesian Methods for Nonlinear Classification and Regression, published by Wiley. Nonlinear 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.

* 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.
Primarily of interest to researchers of nonlinear statistical modelling, the book will also be suitable for graduate students of statistics. The book will benefit researchers involved in regression and classification modelling from electrical engineering, economics, machine learning and computer science.