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Bayesian Theory and Applications
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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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Part I Exchangeability

Part II Hierarchical Models

Part III Markov Chain Monte Carlo

Part IV Dynamic Models

Part V Sequential Monte Carlo

Part VI Nonparametrics

Part VII Spline Models and Copulas

Part VIII Model Elaboration and Prior Distributions

Part IX Regressions and Model Averaging

Part X Finance and Actuarial Science

Part XI Medicine and Biostatistics

Part XII Inverse Problems and Applications