Bayesian Theory and Applications

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.

Table of Contents:
Paul Damien, Petros Dellaportas, Nicholas G. Polson, David A. Stephens: Introduction I EXCHANGEABILITY 1: Michael Goldstein: Observables and Models: exchangeability and the inductive argument 2: A. Philip Dawid: Exchangeability and its Ramifications II HIERARCHICAL MODELS 3: Alan E. Gelfand and Souparno Ghosh: Hierarchical Modeling 4: Sounak Chakraborty, Bani K Mallick and Malay Ghosh: Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification 5: Athanasios Kottas and Kassandra Fronczyk: Flexible Bayesian modelling for clustered categorical responses in developmental toxicology III MARKOV CHAIN MONTE CARLO 6: Siddartha Chib: Markov chain Monte Carlo Methods 7: Jim E. Griffin and David A. Stephens: Advances in Markov chain Monte Carlo IV DYNAMIC MODELS 8: Mike West: Bayesian Dynamic Modelling 9: Dani Gamerman and Esther Salazar: Hierarchical modeling in time series: the factor analytic approach 10: Gabriel Huerta and Glenn A. Stark: Dynamic and spatial modeling of block maxima extremes V SEQUENTIAL MONTE CARLO 11: Hedibert F. Lopes and Carlos M. Carvalho: Online Bayesian learning in dynamic models: An illustrative introduction to particle methods 12: Ana Paula Sales, Christopher Challis, Ryan Prenger, and Daniel Merl: Semi-supervised Classification of Texts Using Particle Learning for Probabilistic Automata VI NONPARAMETRICS 13: Stephen G Walker: Bayesian Nonparametrics 14: Ramsés H. Mena: Geometric Weight Priors and their Applications 15: Stephen G. Walker and George Karabatsos: Revisiting Bayesian Curve Fitting Using Multivariate Normal Mixtures VII SPLINE MODELS AND COPULAS 16: Sally Wood: Applications of Bayesian Smoothing Splines 17: Michael Stanley Smith: Bayesian Approaches to Copula Modelling VIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS 18: M.J. Bayarri and J.O. Berger: Hypothesis Testing and Model Uncertainty 19: E. Gutiérrez-Peña and M. Mendoza: Proper and non-informative conjugate priors for exponential family models 20: David Draper: Bayesian Model Specification: Heuristics and Examples 21: Zesong Liu, Jesse Windle, and James G. Scott: Case studies in Bayesian screening for time-varying model structure: The partition problem IX REGRESSIONS AND MODEL AVERAGING 22: Hugh A. Chipman, Edward I. George and Robert E. McCulloch: Bayesian Regression Structure Discovery 23: Robert B. Gramacy: Gibbs sampling for ordinary, robust and logistic regression with Laplace priors 24: Merlise Clyde and Edwin S. Iversen: Bayesian Model Averaging in the M-Open Framework X FINANCE AND ACTUARIAL SCIENCE 25: Eric Jacquier and Nicholas G Polson: Asset Allocation in Finance: A Bayesian Perspective 26: Arthur Korteweg: Markov Chain Monte Carlo Methods in Corporate Finance 27: Udi Makov: Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special Evolution XI MEDICINE AND BIOSTATISTICS 28: Peter Müller: Bayesian Models in Biostatistics and Medicine 29: Purushottam W. Laud, Siva Sivaganesan and Peter Müller: Subgroup Analysis 30: Timothy E. Hanson and Alejandro Jara: Surviving Fully Bayesian Nonparametric Regression Models XII INVERSE PROBLEMS AND APPLICATIONS 31: Colin Fox, Heikki Haario and J. Andrés Christen: Inverse Problems 32: Jari Kaipio and Ville Kolehmainen: Approximate marginalization over modeling errors and uncertainties in inverse problems 33: C. Nakhleh, D. Higdon, C. K. Allen and R. Ryne: 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.