Bayesian Methods for Nonlinear Classification and Regression
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- 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.
The material available at the link below is 'Matlab code for implementing the examples in the book'.