Bayesian Methods for Nonlinear Classification and Regression Hardcover - 2002 - 1st Edition
by David G. Denison; Christopher C. Holmes; Bani K. Mallick
From the rear cover
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.
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.
Details
- Title Bayesian Methods for Nonlinear Classification and Regression
- Author David G. Denison; Christopher C. Holmes; Bani K. Mallick
- Binding Hardcover
- Edition number 1st
- Edition 1
- Pages 296
- Volumes 1
- Language ENG
- Publisher Wiley
- Date 2002-05-06
- ISBN 9780471490364 / 0471490369
- Weight 1.32 lbs (0.60 kg)
- Dimensions 9.35 x 6.21 x 0.89 in (23.75 x 15.77 x 2.26 cm)
- Library of Congress subjects Bayesian statistical decision theory, Regression analysis
- Library of Congress Catalog Number 2002280834
- Dewey Decimal Code 519.542
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BAYESIAN METHODS FOR NONLINEAR CLASSIFICATION AND REGRESSION
by Denison, D.G.T; Holmes, C.C; Mallick, B.K; Smith, A.F.M.
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