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Bayesian Theory and Applications

A clear, concise description of all the major ideas in Bayesian statistics, brought together for the first time
Includes basic and advanced algorithms aiding anyone who wishes to implement Bayesian models in their own work
Application topics have been included to help practitioners in finance, marketing, psychology, engineering, and physics implement new models in a very straightforward manner

By: Paul Damien (Editor), Petros Dellaportas (Editor), Nicholas G Polson (Editor), David A Stephens (Editor)

720 pages, 21 b/w photos, 121 b/w illustrations

Oxford University Press

Paperback | Feb 2015 | #219597 | ISBN-13: 9780198739074
Availability: Usually dispatched within 6 days Details
NHBS Price: £39.99 $51/€48 approx
Hardback | Jan 2013 | #219598 | ISBN-13: 9780199695607
Availability: Usually dispatched within 6 days Details
NHBS Price: £96.99 $123/€116 approx

About this book

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.

Bayesian Theory and Applications 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. Bayesian Theory and Applications 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, Bayesian Theory and Applications 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.

Bayesian Theory and Applications 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.


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


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Biography

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 A. Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Canada.


Contributors:
Christopher K. Allen, Oak Ridge National Labarotory
Susie J. Bayarri, Universitat de Valencia
James O. Berger, Duke University
Carlos M. Carvalho, University of Texas in Austin
Sounak Chakraborty, University of Missouri-Columbia
Christopher Challis, Duke University
Siddhartha Chib, Washington University
Hugh A. Chipman, Acadia University
Merlise Clyde, Duke University
J. Andrés Christen, CIMAT
Phil Dawid, University of Cambridge
David Draper, University of California in Santa Cruz
Colin Fox, University of Otago
Kassandra Fronczyk, M.D. Anderson Cancer Institute
Dani Gammerman, UFRJ, Brazil
Alan E. Gelfand, Duke University
Edward I. George, University of Pennsylvania
Malay Ghosh, University of Florida
Souparno Ghosh, Duke University
Michael Goldstein, Durham University
Robert B. Gramacy, University of Chicago
Jim E. Griffin, University of Kent
Eduardo Gutiérrez-Peña, IIMAS, UNAM
Heikki Haario, Lappeenranta University of Technology
Timothy E. Hanson, University of South Carolina
David M. Higdon, Los Alamos National Laboratory
Gabriel Huerta, Indiana University
Edwin S. Iversen, Duke University
Eric Jacquier, MIT
Alejandro Jara, Pontifica Universidad Catolica de Chile
Jari Kaipio, University of Auckland
George Karabatsos, University of Illinois
Ville Kolehmainen, University of Eastern Finland
Arthur Korteweg, Stanford University
Athanasios Kottas, University of California Santa Cruz
Purushottam W. Laud, Medical College of Wisconsin
Zesong Liu, University of Texas in Austin
Hedibert F. Lopes, University of Chicago
Udi Makov, University of Haifa
Bani K. Mallick, Texas A & M University
Robert E. McCulloch, University of Chicago
Ramsés H. Mena, IIMAS, UNAM
Manuel Mendoza, ITAM
Daniel Merl, Lawrence Livermore National Laboratory
Peter Müller, University of Texas in Austin
Charles W. Nakhleh, Sandia Labs
Nicholas G. Polson, University of Chicago
Ryan Prenger, Lawrence Livermore National Laboratory
Robert D. Ryne, Lawrence Berkeley National Laboratory
Esther Salazar, Duke University
Ana Paula Sales, Lawrence Livermore National Laboratory
James G. Scott, University of Texas in Austin
Siva Sivaganesan, University of Cincinnati
Michael S. Smith, Melbourne Business School
Glenn A. Stark, University of New Mexico
David A. Stephens, McGill University
Stephen G. Walker
Mike West, Duke University
Jesse Windle, University of Texas in Austin
Sally Wood, Melbourne Business School

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