Jump to ContentJump to Main Navigation
Bayesian Statistics 9 - Oxford Scholarship Online
Users without a subscription are not able to see the full content.

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

Show Summary Details

subscribe or login to access all content.



Front Matter

Integrated Objective Bayesian Estimation and Hypothesis Testing

José M. Bernardo Universitat de Valencia, Spain jose.m.bernardo@uv.es

Dynamic Stock Selection Strategies: A Structured Factor Model Framework*

Carlos M. Carvalho The University of Texas at Austin, USA carlos.carvalho@mccombs.utexas.edu Hedibrt F. Lopes The University of Chicago, USA hlopes@chicagobooth.edu Omar Aguilar Financial Engines, USA o_aguilar@ymail.com

Free Energy Sequential Monte Carlo, Application to Mixture Modelling*

Nicolas Chopin and Pierre Jacob CREST (ENSAE), France nicolas.chopin@ensae.fr   pierre.jacob@ensae.fr

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

Guido Consonni Università di Pavia, Italy guido.consonni@unipv.it Luca La Rocca Università di Modena e Reggio Emilia, Italy luca.larocca@unimore.it

Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels

David B. Dunson Duke University, USA dunson@stat.duke.edu Abhishek Bhattacharya Indian Statistical Institute, India abhishek.sudhir@gmail.com

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

Sylvia Frühwirth‐Schnatter and Helga Wagner Johannes Kepler Universität Linz, Austria sylvia.fruehwirth-schnatter@jku.at   helga.wagner@jku.at

External Bayesian Analysis for Computer Simulators*

Michael Goldstein Durham University, England Michael.Goldstein@durham.ac.uk

Optimization Under Unknown Constraints*

Robert B. Gramacy University of Chicago, USA rbgramacy@chicagobooth.edu Herbert K. H. Lee University of California, Santa Cruz, USA herbie@ams.ucsc.edu

Using TPA for Bayesian Inference*

Mark Huber Claremont McKenna College, USA mhuber@cmc.edu Sarah Schott Duke University, USA schott@math.duke.edu

Nonparametric Bayesian Networks*

Katja Ickstadt TU Dortmund University, Germany ickstadt@statistik.tu-dortmund.de 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 hlopes@chicagobooth.edu Michael S. Johannes Columbia University, USA mj335@columbia.edu Carlos M. Carvalho The University of Texas, Austin, USA carlos.carvalho@mccombs.utexas.edu Nicholas G. Polson The University of Chicago, USA ngp@chicagobooth.edu

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

Thomas J. Loredo Cornell University, USA loredo@astro.cornell.edu

Association Tests that Accommodate Genotyping Uncertainty*

Thomas A. Louis Johns Hopkins Bloomberg School of Public Health, USA tlouis@jhsph.edu 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 madigan@stat.columbia.edu Patrick Ryan GlaxoSmithKline Research and Development, USA ryan@omop.org Shawn Simpson Columbia University, USA shawn@stat.columbia.edu Ivan Zorych Columbia University, USA zorych@gmail.com

Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds

Christopher Meek Microsoft Research,USA meek@microsoft.com Ydo Wexler DRW Trading, USA dowexler@gmail.com

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

Xiao‐Li Meng Harvard University, USA meng@stat.harvard.edu

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

Nicholas G. Polson University of Chicago, USA ngp@chicagobooth.edu James G. Scott University of Texas, USA james.scott@mccombs.utexas.edu

Bayesian Models for Sparse Regression Analysis of High Dimensional Data*

Sylvia Richardson Imperial College London, UK sylvia.richardson@imperial.ac.uk Leonardo Bottolo Imperial College London, UK l.bottolo@imperial.ac.uk Jeffrey S. Rosenthal University of Toronto, Canada jeff@math.toronto.edu

Transparent Parametrizations of Models for Potential Outcomes

Thomas S. Richardson, Robin J. Evans University of Washington, USA thomasr@uw.edu,   rje42@uw.edu James M. Robins Harvard School of Public Health, USA robins@hsph.harvard.edu

Modelling Multivariate Counts Varying Continuously in Space*

Alexandra M. Schmidt Universidade Federal do Rio de Janeiro, Brazil alex@im.ufrj.br Marco A. Rodríguez Université du Québec à Trois‐Rivières, Canada marco.rodriguez@uqtr.ca

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

Claudia Tebaldi University of British Columbia, Canada and Climate Central Inc., USA ctebaldi@climatecentral.org Bruno Sansó University of California Santa Cruz, USA bruno@ams.ucsc.edu Richard L. Smith University of North Carolina, USA rls@email.unc.edu

Bayesian Models for Variable Selection that Incorporate Biological Information*

Marina Vannucci and Francesco C. Stingo Rice University, USA marina@rice.edu    fcs1@rice.edu