Bayesian Methods for Nonlinear Classification and Regression
$381.63
- Hardcover
296 pages
- Release Date
27 March 2002
Summary
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 co…
Book Details
| ISBN-13: | 9780471490364 |
|---|---|
| ISBN-10: | 0471490369 |
| Series: | Wiley Series in Probability and Statistics |
| Author: | David G.T. Denison, Christopher C. Holmes, Bani K. Mallick, Adrian F.M. Smith |
| Publisher: | John Wiley & Sons Inc |
| Imprint: | John Wiley & Sons Inc |
| Format: | Hardcover |
| Number of Pages: | 296 |
| Edition: | 1st |
| Release Date: | 27 March 2002 |
| Weight: | 567g |
| Dimensions: | 233mm x 162mm x 22mm |
What They're Saying
Critics Review
“The exercises and the excellent presentation style make this book qualified t be a textbook in a graduate level nonlinear regression course.” (Journal of Statistical Computation and Simulation, July 2005)
“Its in-depth coverage of implementation issues and detailed discussion of pros and cons of different modeling strategies make it attractive for many researchers.” (Technometrics, May 2004)
”…a fascinating account of a rapidly evolving area of statistics…” (Short Book Reviews, December 2002)
”…will benefit researchers…also suitable for graduate students…” (Mathematical Reviews, 2003m)
About The Author
David G.T. Denison
David G. T. Denison and Christopher C. Holmes are the authors of Bayesian Methods for Nonlinear Classification and Regression, published by Wiley.
Returns
This item is eligible for free returns within 30 days of delivery. See our returns policy for further details.