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Bayesian Statistics 9
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Bayesian Statistics 9

José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West


The Valencia International Meetings on Bayesian Statistics – established in 1979 and held every four years – have been the forum for a definitive overview of current concerns and activities in Bayesian statistics. These are the edited Proceedings of the Ninth meeting, and contain the invited papers each followed by their discussion and a rejoinder by the author(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, highlighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as ... More

Keywords: Bayesian, Valencia, Bayes’ Theorem, frequentist, conference, hypothesis testing, prior, posterior, distribution, inference, MCMC, Markov chain, Monte Carlo methods, Statistics

Bibliographic Information

Print publication date: 2011 Print ISBN-13: 9780199694587
Published to Oxford Scholarship Online: January 2012 DOI:10.1093/acprof:oso/9780199694587.001.0001


Affiliations are at time of print publication.

José M. Bernardo, editor
Universitat de València

M. J. Bayarri, editor
Universitat de València

James O. Berger, editor
Duke University

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Front Matter

Integrated Objective Bayesian Estimation and Hypothesis Testing

José M. Bernardo Universitat de Valencia, Spain

Dynamic Stock Selection Strategies: A Structured Factor Model Framework*

Carlos M. Carvalho The University of Texas at Austin, USA Hedibrt F. Lopes The University of Chicago, USA Omar Aguilar Financial Engines, USA

Free Energy Sequential Monte Carlo, Application to Mixture Modelling*

Nicolas Chopin and Pierre Jacob CREST (ENSAE), France

Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs*

Guido Consonni Università di Pavia, Italy Luca La Rocca Università di Modena e Reggio Emilia, Italy

Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels

David B. Dunson Duke University, USA Abhishek Bhattacharya Indian Statistical Institute, India

Bayesian Variable Selection for Random Intercept Modeling of Gaussian and Non‐Gaussian Data

Sylvia Frühwirth‐Schnatter and Helga Wagner Johannes Kepler Universität Linz, Austria

External Bayesian Analysis for Computer Simulators*

Michael Goldstein Durham University, England

Optimization Under Unknown Constraints*

Robert B. Gramacy University of Chicago, USA Herbert K. H. Lee University of California, Santa Cruz, USA

Using TPA for Bayesian Inference*

Mark Huber Claremont McKenna College, USA Sarah Schott Duke University, USA

Nonparametric Bayesian Networks*

Katja Ickstadt TU Dortmund University, Germany BjörnBornkamp, Marco Grzegorczyk, Jakob Wieczorek TU Dortmund University, Germany Malik R. Sheriff, Hernán E. Grecco and Eli Zamir Max‐Planck Institute of Molecular Physiology, Dortmund, Germany

Particle Learning for Sequential Bayesian Computation*

Hedibert F. Lopes The University of Chicago, USA Michael S. Johannes Columbia University, USA Carlos M. Carvalho The University of Texas, Austin, USA Nicholas G. Polson The University of Chicago, USA

Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky*

Thomas J. Loredo Cornell University, USA

Association Tests that Accommodate Genotyping Uncertainty*

Thomas A. Louis Johns Hopkins Bloomberg School of Public Health, USA Benilton S. Carvalho University of Cambridge, UK M. Daniele Fallin Johns Hopkins BSPH, USA Rafael A. Irizarryi Johns Hopkins BSPH, USA Qing Li NIH Human Genome Res. Inst., USA Ingo Ruczinski Johns Hopkins BSPH, USA

Bayesian Methods in Pharmacovigilance*

David Madigan Columbia University, USA Patrick Ryan GlaxoSmithKline Research and Development, USA Shawn Simpson Columbia University, USA Ivan Zorych Columbia University, USA

Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds

Christopher Meek Microsoft Research,USA Ydo Wexler DRW Trading, USA

What's the H in H‐likelihood: A Holy Grail or an Achilles' Heel?*

Xiao‐Li Meng Harvard University, USA

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction*

Nicholas G. Polson University of Chicago, USA James G. Scott University of Texas, USA

Bayesian Models for Sparse Regression Analysis of High Dimensional Data*

Sylvia Richardson Imperial College London, UK Leonardo Bottolo Imperial College London, UK Jeffrey S. Rosenthal University of Toronto, Canada

Transparent Parametrizations of Models for Potential Outcomes

Thomas S. Richardson, Robin J. Evans University of Washington, USA, James M. Robins Harvard School of Public Health, USA

Modelling Multivariate Counts Varying Continuously in Space*

Alexandra M. Schmidt Universidade Federal do Rio de Janeiro, Brazil Marco A. Rodríguez Université du Québec à Trois‐Rivières, Canada

Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models*

Claudia Tebaldi University of British Columbia, Canada and Climate Central Inc., USA Bruno Sansó University of California Santa Cruz, USA Richard L. Smith University of North Carolina, USA

Bayesian Models for Variable Selection that Incorporate Biological Information*

Marina Vannucci and Francesco C. Stingo Rice University, USA