OXFORD UNIVERSITY PRESS

Bayesian Theory and Applications

ISBN : 9780199695607

Price(incl.tax): 
¥24,651
Author: 
Paul Damien; Petros Dellaportas; Nicholas G. Polson; David A. Stephens
Pages
720 Pages
Format
Hardcover
Size
162 x 240 mm
Pub date
Jan 2013
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The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and developments, and who may be looking for ideas that could spawn new research. Hence, the audience for this unique book would likely include academicians/practitioners, and could likely be required reading for undergraduate and graduate students in statistics, medicine, engineering, scientific computation, business, psychology, bio-informatics, computational physics, graphical models, neural networks, geosciences, and public policy. The book honours the contributions of Sir Adrian F. M. Smith, one of the seminal Bayesian researchers, with his papers on hierarchical models, sequential Monte Carlo, and Markov chain Monte Carlo and his mentoring of numerous graduate students -the chapters are authored by prominent statisticians influenced by him. Bayesian Theory and Applications should serve the dual purpose of a reference book, and a textbook in Bayesian Statistics.

Index: 

Introduction
I EXCHANGEABILITY
1. Observables and Models: exchangeability and the inductive argument
2. Exchangeability and its Ramifications
II HIERARCHICAL MODELS
3. Hierarchical Modeling
4. Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification
5. Flexible Bayesian modelling for clustered categorical responses in developmental toxicology
III MARKOV CHAIN MONTE CARLO
6. Markov chain Monte Carlo Methods
7. Advances in Markov chain Monte Carlo
IV DYNAMIC MODELS
8. Bayesian Dynamic Modelling
9. Hierarchical modeling in time series: the factor analytic approach
10. Dynamic and spatial modeling of block maxima extremes
V SEQUENTIAL MONTE CARLO
11. Online Bayesian learning in dynamic models: An illustrative introduction to particle methods
12. Semi-supervised Classification of Texts Using Particle Learning for Probabilistic Automata
VI NONPARAMETRICS
13. Bayesian Nonparametrics
14. Geometric Weight Priors and their Applications
15. Revisiting Bayesian Curve Fitting Using Multivariate Normal Mixtures
VII SPLINE MODELS AND COPULAS
16. Applications of Bayesian Smoothing Splines
17. Bayesian Approaches to Copula Modelling
VIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS
18. Hypothesis Testing and Model Uncertainty
19. Proper and non-informative conjugate priors for exponential family models
20. Bayesian Model Specification: Heuristics and Examples
21. Case studies in Bayesian screening for time-varying model structure: The partition problem
IX REGRESSIONS AND MODEL AVERAGING
22. Bayesian Regression Structure Discovery
23. Gibbs sampling for ordinary, robust and logistic regression with Laplace priors
24. Bayesian Model Averaging in the M-Open Framework
X FINANCE AND ACTUARIAL SCIENCE
25. Asset Allocation in Finance: A Bayesian Perspective
26. Markov Chain Monte Carlo Methods in Corporate Finance
27. Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special Evolution
XI MEDICINE AND BIOSTATISTICS
28. Bayesian Models in Biostatistics and Medicine
29. Subgroup Analysis
30. Surviving Fully Bayesian Nonparametric Regression Models
XII INVERSE PROBLEMS AND APPLICATIONS
31. Inverse Problems
32. Approximate marginalization over modeling errors and uncertainties in inverse problems
33. Bayesian reconstruction of particle beam phase space

About the author: 

Paul Damien is a Professor at the McCombs School of Business, University of Texas in Austin.; Petros Dellaportas is a Professor at the Athens University of Economics and Business.; Nicholas G Polson is Professor of Econometrics and Statistics at Chicago Booth, University of Chicago.; David M Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Canada.

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