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Bayesian Methods for Nonlinear Classification and Regression
David G. T. Denison
€ 168.73
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Description for Bayesian Methods for Nonlinear Classification and Regression
Hardcover. Regression analysis models the relationship between a set of responses and another variable: for example, to estimate the true position of a line through a number of observed points. Unfortunately, data rarely conforms to simple curves and straight lines - parametric models - and this text examines more complex - or nonparametric - models. Series: Wiley Series in Probability and Statistics. Num Pages: 294 pages, Ill. BIC Classification: PBK; PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 243 x 170 x 22. Weight in Grams: 582.
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.
... Read more
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.
... Read more
Product Details
Format
Hardback
Publication date
2002
Publisher
John Wiley and Sons Ltd United Kingdom
Number of pages
294
Condition
New
Series
Wiley Series in Probability and Statistics
Number of Pages
296
Place of Publication
New York, United States
ISBN
9780471490364
SKU
V9780471490364
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About 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.
Reviews for Bayesian Methods for Nonlinear Classification and Regression
"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 ... Read more