Bayesian Theory and Applications

NOTE EDITORE

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.

SOMMARIO

1 - Observables and Models: exchangeability and the inductive argument2 - Exchangeability and its Ramifications3 - Hierarchical Modeling4 - Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification5 - Flexible Bayesian modelling for clustered categorical responses in developmental toxicology6 - Markov chain Monte Carlo Methods7 - Advances in Markov chain Monte Carlo8 - Bayesian Dynamic Modelling9 - Hierarchical modeling in time series: the factor analytic approach10 - Dynamic and spatial modeling of block maxima extremes11 - Online Bayesian learning in dynamic models: An illustrative introduction to particle methods12 - Semi-supervised Classification of Texts Using Particle Learning for Probabilistic Automata13 - Bayesian Nonparametrics14 - Geometric Weight Priors and their Applications15 - Revisiting Bayesian Curve Fitting Using Multivariate Normal Mixtures16 - Applications of Bayesian Smoothing Splines17 - Bayesian Approaches to Copula Modelling18 - Hypothesis Testing and Model Uncertainty19 - Proper and non-informative conjugate priors for exponential family models20 - Bayesian Model Specification: Heuristics and Examples21 - Case studies in Bayesian screening for time-varying model structure: The partition problem22 - Bayesian Regression Structure Discovery23 - Gibbs sampling for ordinary, robust and logistic regression with Laplace priors24 - Bayesian Model Averaging in the M-Open Framework25 - Asset Allocation in Finance: A Bayesian Perspective26 - Markov Chain Monte Carlo Methods in Corporate Finance27 - Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special Evolution28 - Bayesian Models in Biostatistics and Medicine29 - Subgroup Analysis30 - Surviving Fully Bayesian Nonparametric Regression Models31 - Inverse Problems32 - Approximate marginalization over modeling errors and uncertainties in inverse problems33 - Bayesian reconstruction of particle beam phase space

AUTORE

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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780199695607
• Dimensioni: 240 x 46.7 x 162 mm Ø 1210 gr
• Formato: Copertina rigida
• Illustration Notes: 121 b/w line drawings & 21 b/w halftones
• Pagine Arabe: 720