Bayesian Theory and Applications by Paul Damien

Bayesian Theory and Applications

EditorPaul Damien, Petros Dellaportas, Nicholas G. Polson

Paperback | May 6, 2015

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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 advancefollowed 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 amore 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, graphicalmodels, 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 statisticiansinfluenced by him.Bayesian Theory and Applications should serve the dual purpose of a reference book, and a textbook in Bayesian Statistics.

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 M...
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Details & Specs

Title:Bayesian Theory and ApplicationsFormat:PaperbackDimensions:720 pages, 9.21 × 6.14 × 0 inPublished:May 6, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198739079

ISBN - 13:9780198739074

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Table of Contents

Paul Damien, Petros Dellaportas, Nicholas G. Polson, David A. Stephens: IntroductionI EXCHANGEABILITY1. Michael Goldstein: Observables and Models: exchangeability and the inductive argument2. A. Philip Dawid: Exchangeability and its RamificationsII HIERARCHICAL MODELS3. Alan E. Gelfand and Souparno Ghosh: Hierarchical Modeling4. Sounak Chakraborty, Bani K Mallick and Malay Ghosh: Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification5. Athanasios Kottas and Kassandra Fronczyk: Flexible Bayesian modelling for clustered categorical responses in developmental toxicologyIII MARKOV CHAIN MONTE CARLO6. Siddartha Chib: Markov chain Monte Carlo Methods7. Jim E. Griffin and David A. Stephens: Advances in Markov chain Monte CarloIV DYNAMIC MODELS8. Mike West: Bayesian Dynamic Modelling9. Dani Gamerman and Esther Salazar: Hierarchical modeling in time series: the factor analytic approach10. Gabriel Huerta and Glenn A. Stark: Dynamic and spatial modeling of block maxima extremesV SEQUENTIAL MONTE CARLO11. Hedibert F. Lopes and Carlos M. Carvalho: Online Bayesian learning in dynamic models: An illustrative introduction to particle methods12. Ana Paula Sales, Christopher Challis, Ryan Prenger, and Daniel Merl: Semi-supervised Classification of Texts Using Particle Learning for Probabilistic AutomataVI NONPARAMETRICS13. Stephen G Walker: Bayesian Nonparametrics14. Ramses H. Mena: Geometric Weight Priors and their Applications15. Stephen G. Walker and George Karabatsos: Revisiting Bayesian Curve Fitting Using Multivariate Normal MixturesVII SPLINE MODELS AND COPULAS16. Sally Wood: Applications of Bayesian Smoothing Splines17. Michael Stanley Smith: Bayesian Approaches to Copula ModellingVIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS18. M.J. Bayarri and J.O. Berger: Hypothesis Testing and Model Uncertainty19. E. Gutierrez-Pena and M. Mendoza: Proper and non-informative conjugate priors for exponential family models20. David Draper: Bayesian Model Specification: Heuristics and Examples21. Zesong Liu, Jesse Windle, and James G. Scott: Case studies in Bayesian screening for time-varying model structure: The partition problemIX REGRESSIONS AND MODEL AVERAGING22. Hugh A. Chipman, Edward I. George and Robert E. McCulloch: Bayesian Regression Structure Discovery23. Robert B. Gramacy: Gibbs sampling for ordinary, robust and logistic regression with Laplace priors24. Merlise Clyde and Edwin S. Iversen: Bayesian Model Averaging in the M-Open FrameworkX FINANCE AND ACTUARIAL SCIENCE25. Eric Jacquier and Nicholas G Polson: Asset Allocation in Finance: A Bayesian Perspective26. Arthur Korteweg: Markov Chain Monte Carlo Methods in Corporate Finance27. Udi Makov: Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special EvolutionXI MEDICINE AND BIOSTATISTICS28. Peter Muller: Bayesian Models in Biostatistics and Medicine29. Purushottam W. Laud, Siva Sivaganesan and Peter Muller: Subgroup Analysis30. Timothy E. Hanson and Alejandro Jara: Surviving Fully Bayesian Nonparametric Regression ModelsXII INVERSE PROBLEMS AND APPLICATIONS31. Colin Fox, Heikki Haario and J. Andres Christen: Inverse Problems32. Jari Kaipio and Ville Kolehmainen: Approximate marginalization over modeling errors and uncertainties in inverse problems33. C. Nakhleh, D. Higdon, C. K. Allen and R. Ryne: Bayesian reconstruction of particle beam phase space