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Other titles in the Wiley Series in Probability and Statistics series:

Bayesian Methods for Nonlinear Classification and Regression (Wiley Series in Probability & Statistics)

by

Bayesian Methods for Nonlinear Classification and Regression (Wiley Series in Probability & Statistics) Cover

 

Synopses & Reviews

Publisher Comments:

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.

The material available at the link below is 'Matlab code for implementing the examples in the book'.

http://stats.ma.ic.ac.uk/~ccholmes/Book_code/book_code.html

Book News Annotation:

Denison (Imperial College of Science, Technology and Medicine, UK) brings together, in a consistent statistical framework, the ideas of nonlinear modeling and Bayesian methods, focusing on data-driven approaches to classification and regression. Primarily of interest to researchers of nonlinear statistical modeling, the book is also suitable for graduate students in statistics, and will benefit researchers involved in regression and classification modeling from electrical engineering, machine learning, and computer science. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

"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)

Synopsis:

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.

Table of Contents

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.

Product Details

ISBN:
9780471490364
Author:
Denison, David
Author:
Holmes, Chris
Author:
Holmes, Christopher C.
Author:
Denison, David G. T.
Author:
Smith, Adrian F. M.
Author:
Denison
Author:
Mallick, Bani K.
Publisher:
John Wiley & Sons
Location:
Chichester, England
Subject:
Regression analysis
Subject:
Bayesian statistical decision theory
Subject:
Nonparametric statistics
Subject:
Probability & Statistics - General
Subject:
Probability & Statistics - Bayesian Analysis
Subject:
Probability & Statistics - Regression Analysis
Subject:
Statistics
Subject:
Mathematics - General
Subject:
Bayesian analysis
Copyright:
Edition Description:
Includes bibliographical references and index.
Series:
Wiley Series in Probability and Statistics
Series Volume:
386
Publication Date:
20020527
Binding:
HARDCOVER
Grade Level:
General/trade
Language:
English
Illustrations:
Yes
Pages:
296
Dimensions:
233 x 162 x 22.2 mm 21 oz

Related Subjects

Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Bayesian Methods for Nonlinear Classification and Regression (Wiley Series in Probability & Statistics) New Hardcover
0 stars - 0 reviews
$178.25 In Stock
Product details 296 pages John Wiley & Sons - English 9780471490364 Reviews:
"Synopsis" by , "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)

"Synopsis" by , 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.

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