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Bayesian methods for nonlinear classification and regression

Author: David G T Denison
Publisher: Chichester, England ; New York, NY : Wiley, ©2002.
Series: Wiley series in probability and statistics.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

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,  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: David G T Denison
ISBN: 0471490369 9780471490364
OCLC Number: 48754149
Description: 277 pages : illustrations ; 24 cm.
Contents: Regression and Classification --
Bayesian Nonlinear Methods --
Approximating functions --
The 'best' model --
Bayesian methods --
Bayesian Modelling --
Data Modelling --
The representation theorem for classification --
The general representation theorem --
Bayes' Theorem --
Modelling with predictors --
Basics of Regression Modelling --
The regression problem --
Basis function models for the regression function --
The Bayesian Linear Model --
The priors --
The likelihood --
The posterior --
Model Comparison --
Bayes' factors --
Occam's razor --
Lindley's paradox --
Model Selection --
Searching for models --
Model Averaging --
Predictive inference --
Problems with model selection --
Posterior Sampling --
The Gibbs sampler --
The Metropolis-Hastings algorithm --
The reversible jump algorithm --
Hybrid sampling --
Convergence --
Curve Fitting --
Curve Fitting Using Step Functions --
Example: Nile discharge data --
Curve Fitting with Splines --
Metropolis-Hastings sampler --
Gibbs sampling --
Example: Great Barrier Reef Data --
Monitoring convergence of the sampler --
Default curve fitting --
Curve Fitting Using Wavelets --
Wavelet shrinkage --
Bayesian wavelets --
Prior Elicitation --
The model prior --
Prior on the model parameters --
The prior on the coefficients --
The prior on the regression variance --
Robust Curve Fitting --
Modelling with a heavy-tailed error distribution --
Outlier detection models --
Surface Fitting --
Additive Models --
Introduction to additive modelling --
Ozone data example.
Series Title: Wiley series in probability and statistics.
Responsibility: David G.T. Denison [and others].
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"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 Read more...

 
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