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. 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

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