Bayesian Methods for Nonlinear Classification and Regression ... - 9780471490364

Bayesian Methods for Nonlinear Classification and RegressionAuthor(s): David G. T. Denison, Christopher C. Holmes, Bani K. Mallick, Adrian F. M. Smith\nFormat: Hardback\nPublisher: John Wiley & Sons Inc, United States\nImprint: John Wiley & Sons Inc\nISBN-13: 9780471490364, 978-0471490364\nSynopsis\nNonlinear 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.\n * Focuses on the problems of classification and regression using flexible, data-driven approaches.\n * .