Probability & Statistics, Statistics 

Wiley Series in Probability and Statistics 

Nonlinear Bayesian modelling is a relatively new field, but onethat has seen a recent explosion of interest. Nonlinear modelsoffer more flexibility than those with linear assumptions, andtheir implementation has now become much easier due to increases incomputational power. Bayesian methods allow for the incorporationof prior information, allowing the user to make coherent inference.Bayesian Methods for Nonlinear Classification and Regression is thefirst book to bring together, in a consistent statisticalframework, the ideas of nonlinear modelling and Bayesian methods.Focuses on the problems of classification and regression usingflexible, data-driven approaches.Demonstrates how Bayesian ideas can be used to improve existingstatistical methods.Includes coverage of Bayesian additive models, decision trees,nearest-neighbour, wavelets, regression splines, and neuralnetworks.Emphasis is placed on sound implementation of nonlinearmodels.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 datasets.Primarily of interest to researchers of nonlinear statisticalmodelling, the book will also be suitable for graduate students ofstatistics. The book will benefit researchers involved inregressionand classification modelling from electrical engineering,economics, machine learning and computer science. The material available at the link below is 'Matlab codefor implementing the examples in the book'.http://stats.ma.ic.ac.uk/~ccholmes/Book_code/book_code.html - from Amzon