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Bayesian Theory and Applications

Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Abstract

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 book travels on 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 honours the contributions of Sir Adrian F. M. Smith, one of the seminal Bayesian researchers, with his work on hierarchical models, sequent ... More

Keywords: hierarchical models, Markov chain Monte Carlo, MCMC techniques, Bayesian analysis, Bayesian statistics, Bayesian theory

Bibliographic Information

Print publication date: 2013 Print ISBN-13: 9780199695607
Published to Oxford Scholarship Online: May 2013 DOI:10.1093/acprof:oso/9780199695607.001.0001

Authors

Affiliations are at time of print publication.

Paul Damien, editor
Professor, McCombs School of Business, University of Texas in Austin

Petros Dellaportas, editor
Professor, Athens University of Economics and Business

Nicholas G. Polson, editor
Professor of Econometrics and Statistics, Chicago Booth, University of Chicago

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Contents

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Front Matter

Title Pages

Dedication

Contributors

Introduction

Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Part I Exchangeability

1 Observables and models: exchangeability and the inductive argument

Micheal Goldstein

2 Exchangeability and its ramifications

A. Philip Dawid

Part II Hierarchical Models

ALAN E. GELFAND AND SOUPARNO GHOSH

3 Hierarchical modelling

Alan E Gelfand, and Souparno Ghosh

4 Bayesian hierarchical kernel machines for nonlinear regression and classification

Sounak Chakraborty, Bani K. Mallick and Malay Ghosh

5 Flexible Bayesian modelling for clustered categorical responses in developmental toxicology

Athanasios Kottas and Kassandra Fronczyk

Part III Markov Chain Monte Carlo

6 Markov chain Monte Carlo methods

Siddhartha Chib

7 Advances in Markov chain Monte Carlo

Jim E. Griffin and David A. Stephens

Part IV Dynamic Models

8 Bayesian dynamic modelling

Mike West

9 Hierarchical modelling in time series: the factor analytic approach

Dani Gamerman and Esther Salazar

10 Dynamic and spatial modelling of block maxima extremes

Gabriel Huerta and Glenn A. Stark

Part V Sequential Monte Carlo

11 Online Bayesian learning in dynamic models: an illustrative introduction to particle methods

HEDIBERT F. LOPES AND CARLOS M.CARVALHO

12 Semi-supervised classification of texts using particle learning for probabilistic automata

ANA PAULA SALES, CHRISTOPHER CHALLIS, RYAN PRENGER AND DANIEL MERL

Part VI Nonparametrics

13 Bayesian nonparametrics

Stephen G. Walker

14 Geometric weight priors and their applications

RamsÉs H. Mena

15 Revisiting Bayesian curve fitting using multivariate normal mixtures∗

Stephen G. Walker and George Karabatsos

Part VII Spline Models and Copulas

16 Applications of Bayesian smoothing splines

Sally Wood

17 Bayesian approaches to copula modelling

Michael Stanley Smith

Part VIII Model Elaboration and Prior Distributions

18 Hypothesis testing and model uncertainty

M. J. Bayarri and J. O. Berger

19 Proper and non-informative conjugate priors for exponential family models

E. GUTIéRREZ - PEñA AND M. MENDOZA

20 Bayesian Model Specification: Heuristics And Examples

David Draper

21 Case studies in Bayesian screening for time-varying model structure: the partition problem

Zesong Liu, Jesse Windle and James G. Scott

Part IX Regressions and Model Averaging

22 Bayesian regression structure discovery

Hugh A. Chipman, Edward I. George and Robert E. Mcculloch

23 Gibbs sampling for ordinary, robust and logistic regression with Laplace priors

Robert B. Gramacy

24 Bayesian model averaging in the M-open framework

Merlise Clyde and Edwin S. Iversen

Part X Finance and Actuarial Science

25 Asset allocation in finance: a Bayesian perspective

Eric Jacquier and Nicholas G. Polson

26 Markov chain Monte Carlo methods in corporate finance

Arthur Korteweg

27 Actuarial credibility theory and Bayesian statistics—the story of a special evolution

Udi Makov

Part XI Medicine and Biostatistics

28 Bayesian models in biostatistics and medicine

Peter MüLler

29 Subgroup analysis

Purushottam W. Laud, Siva Sivaganesan and Peter MüLler

30 Surviving fully Bayesian nonparametric regression models

Timothy E. Hanson and Alejandro Jara

Part XII Inverse Problems and Applications

31 Inverse problems

Colin Fox, Heikki Haario and J. Andrés Christen

32 Approximate marginalization over modelling errors and uncertainties in inverse problems

Jari Kaipio and Ville Kolehmainen

33 Bayesian reconstruction of particle beam phase space

C. Nakhleh, D. Higdon, C. K. Allen and R. Ryne

End Matter

Adrian Smith’s research supervision (PhD)

Adrian Smith’s publications

INDEX