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Valencia International Meetings on Bayesian Statistics

Bayesian Statistics, a definition

Statistics is primarily concerned with the analysis of data, either to assist in the appreciation of some underlying mechanism, or to reach effective decisions. In both cases, some uncertainty resides in the situation and the statistician's tasks are both to reduce this uncertainty and to explain it clearly. Problems of this type occur throughout all the physical, social and other sciences. One way of looking at statistics stems from the appreciation that all uncertainty must be described by probability: that probability is the only sensible language for a logic that deals with all degrees of uncertainty, and not just with the extremes of truth and falsity. This is called Bayesian Statistics. Decision-making is embraced by introducing a utility function, itself probability-based, and then maximizing expected utility. Bayesian statistics is designed to handle all situations where uncertainty is found. Since some uncertainty is present in most aspects of life, it is held that Bayesian statistics should be appreciated and used by everyone. It is the logic of contemporary society. It is 'common sense reduced to calculation.'

For an elementary, college level, introduction to Bayesian Statistics, see, for example, Bernardo (2003) Bayesian Statistics, an article written for the UNESCO Encyclopedia of Life Support Systems (EOLSS).

The Valencia Meetings

Every four years since 1979, the University of Valencia, Spain, has sponsored meetings devoted to Bayesian Statistics, the Valencia International Meetings on Bayesian Statistics. The published proceedings are listed below. The tremendous number of citations generated by them underscores their scientific relevance. The numbers attending have dramatically increased and include not only specialist statisticians but also others whose work has uncertainty as an important ingredient.

1. J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith (eds). (1980).
   Bayesian Statistics. Valencia: University Press.
   Full text available at (better quality, not searchable) Valencia1.pdf or (lesser quality, searchable) Valencia1.pdf
2. J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith (eds). (1985). Bayesian Statistics 2. Amsterdam: North-Holland.
   Full text available at Valencia2.pdf
3. J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith (eds).(1988).
   Bayesian Statistics 3. Oxford: Oxford University Press.
4. J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds). (1992).
   Bayesian Statistics 4. Oxford: Oxford University Press.
5. J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds). (1996).
   Bayesian Statistics 5. Oxford: Oxford University Press.
6. J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds). (1999).
   Bayesian Statistics 6. Oxford: Oxford University Press.
   Contents available at Valencia6Contents.pdf
7. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (eds). (2003).
   Bayesian Statistics 7. Oxford: Oxford University Press.
   Preface and contents available at Valencia7Contents.pdf
8. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (eds). (2007).
   Bayesian Statistics 8. Oxford: Oxford University Press.
   Preface and contents available at Valencia8Contents.pdf
9. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (eds). (2011).
   Bayesian Statistics 9. Oxford: Oxford University Press (in preparation)
   

For details on the origin and development of the Valencia International Meetings on Bayesian Statistics, click The Valencia Meetings.pdf

The Valencia Meetings are Equal Opportunity / Affirmative Action Conferences.

Co-sponsored by the Universitat de València (UV) and by the International Society for Bayesian Analysis (ISBA), the 9th Valencia International Meeting on Bayesian Statistics and the 2010 World Meeting of the International Society for Bayesian Analysis were jointly held in Benidorm (Alicante, Spain) from June 3rd to June 8th, 2010.

As previously announced, this has been the last of the Valencia International Meetings on Bayesian Statistics. In the future, Bayesians will have a World Meeting organized by ISBA every two years. The next two venues will be Kyoto (Japan) in 2012 and Cancún (Mexico) in 2014.

Local Organizer

• José M. Bernardo (Universitat de València, Spain) jose.m.bernardo@uv.es

Programme Committee

• Susie Bayarri (Universitat de València, Spain) [susie.bayarri at uv.es]
• James O. Berger (Duke University, USA) [berger at stat.duke.edu]
• José M. Bernardo (Universitat de València, Spain) [jose.m.bernardo at uv.es]
• A. Philip Dawid (University of Cambridge, UK) [a.p.dawid at statslab.cam.ac.uk]
• David Heckerman (Microsoft Research, USA) [heckerma at microsoft.com]
• Adrian F. M. Smith (Director General for Science and Research, UK) [afmsmith at gmail.com]
• Mike West (Duke University, USA) [mw at stat.duke.edu]

Acknowlegements

Venue

As in previous occasions, this was a residential conference in a coastal resort. On this occasion, as in Valencia 8, the venue was Gran Hotel Bali (see picture below), a four star hotel with an appropriate purpose built large auditorium. The hotel is located at the southern end of Benidorm (50 km north from Alicante and 140 km south from Valencia.

Conference Programme

Valencia 9 Invited Programme

• Bernardo, José M. (Universitat de València, Spain)
  Integrated objective Bayesian estimation and hypothesis testing. Bernardo.pdf
  Discussant: Pericchi, Luis (Universidad de Puerto Rico, Rio Piedras, Puerto Rico)

• Carvalho, Carlos (University of Chicago and University of Texas at Austin, USA)
  Dynamic stock selection strategies: A structured factor model framework. Carvalho.pdf
  Discussant: Mendoza, Manuel (ITAM, Mexico)

• Chopin, Nicolas (ENSAE, France)
  Free energy sequential Monte Carlo, application to mixture modelling. Chopin.pdf
  Discussant: Green, Peter (University of Bristol, UK)

• Consonni, Guido (Università di Pavia, Italy)
  On moment priors for Bayesian model choice with applications to directed acyclic graphs. Consonni.pdf
  Discussant: Smith, James Q. (University of Warwick, UK)

• Dunson, David (Duke University, USA)
  Nonparametric Bayes classification and testing on manifolds. Dunson.pdf
  Discussant: Griffin, James (University of Kent, UK)

• Frühwirth-Schnatter, Sylvia (Johannes Kepler Universität Linz, Austria)
  Bayesian variable selection for random intercept modeling of Gaussian and non-Gaussian data. Fruehwirth.pdf
  Discussant: Brown, Philip (University of Kent, UK)

• Goldstein, Michael (University of Durham, UK)
  External Bayesian analysis for computer simulators. Goldstein.pdf
  Discussant: Paulo, Rui (Universidade Técnica de Lisboa, Portugal)

• Huber, Mark (Claremont McKenna College, USA)
  Using TPA for Bayesian inference. Huber.pdf
  Discussant: Roberts, Gareth (University of Warwick, UK)

• Ickstadt, Katja (Technische Universität Dortmund, Germany)
  Nonparametric Bayesian networks. Ickstadt.pdf
  Discussant: Jordan, Michael (University of California, Berkeley, USA)

• Lee, Herbie (University of California, Santa Cruz, USA)
  Optimization under unknown constraints Lee.pdf
  Discussant: Holmes, Christopher (University of Oxford, UK)

• Lopes, Hedibert (University of Chicago, USA)
  Particle learning for sequential Bayesian computation. Lopes.pdf
  Discussant: Pitt, Michael (University of Warwick, UK)

• Loredo, Tom (Cornell University, USA)
  Rotating stars and revolving planets: Bayesian exploration of the pulsating sky Loredo.pdf
  Discussant: Müller, Peter (MD Anderson Cancer Center, Texas, USA)

• Louis, Tom (Johns Hopkins University, USA)
  Association tests that accommodate genotyping uncertainty. Louis.pdf
  Discussant: Dukic, Vanja (University of Chicago, USA)

• Madigan, David (Columbia University, USA)
  Bayesian methods in pharmacovigilance. Madigan.pdf
  Discussant: DuMouchel, William (Phase Forward Inc., USA)

• McCallum, Andrew (University of Massachusetts, USA)
  Probabilistic programming with imperatively-defined factor graphs. McCallum.pdf
  Discussant: Ghahramani, Zoubin (University of Cambridge, UK)

• Meek, Christopher (Microsoft Research, USA)
  Improved approximate sum-product inference using multiplicative error bounds. Meek.pdf
  Discussant: Mira, Antonietta (Università dell'Insubria, Italy)

• Meng, Xiao-Li (Harvard University, USA)
  What's the H in H-likelihood: A Holy Grail or an Achilles' Heel? Meng.pdf
  Discussant: George, Edward (University of Pennsylvania, USA)

• Polson, Nicholas (University of Chicago, USA)
  Shrink globally, act locally: Sparse Bayesian regularization and prediction. Polson.pdf
  Discussant: Clarke, Bertrand (University of Miami, USA)

• Richardson, Sylvia (Imperial College London, UK)
  Bayesian models for sparse regression analysis of high dimensional data. RichardsonS.pdf
  Discussant: Mallick, Bani (Texas A&M University, USA)

• Richardson, Thomas (University of Washington, USA)
  Transparent parametrizations of models for potential outcomes. RichardsonT.pdf
  Discussant: Fienberg, Stephen (Carnegie-Mellon University, USA)

• Schmidt, Alexandra (Universidade Federal do Rio de Janeiro, Brazil)
  Modelling multivariate counts varying continuously in space. Schmidt.pdf
  Discussant: Boys, Richard (University of Newcastle, UK)

• Tebaldi, Claudia (Climate Central, USA, and University of British Columbia, Canada)
  Characterizing uncertainty of future climate change projections using hierarchical Bayesian models. Tebaldi.pdf
  Discussant: Ferreira, Marco (University of Missouri, USA)

• Vannucci, Marina (Rice University, USA)
  Bayesian models for variable selection that incorporate biological information. Vannucci.pdf
  Discussant: Berzuini, Carlo (University of Cambridge, UK)

• Wilkinson, Darren (University of Newcastle, UK)
  Parameter inference for stochastic kinetic models of bacterial gene regulation: a Bayesian approach to systems biology. Wilkinson.pdf
  Discussant: Kou, Samuel (University of Harvard, USA)

ISBA 2010 Invited Programme

A separate programme of 36 selected oral contributions was organized by the ISBA program committee. The complete list of these (in alphabetical order of speakers) is below.

• Adams, Ryan (University of Toronto, Canada)
  Generative modeling of probability densities with Gaussian processes.
• Airoldi, Edo (Harvard University, USA)
  Representation and Bayesian analysis of integer-valued networks
• Almeida, Carlos (Technische Universität München, Germany)
  Bayesian inference for time-varying pair-copula constructions.
• Astle, William (Imperial College, UK)
  A Bayesian model of NMR spectra for the deconvolution and quantification of metabolites in complex biological mixtures.
• Bhattacharya, Anirban (Duke University, USA)
  Sparse Bayesian infinite factor models.
• Draper, David (University of California, Santa Cruz, USA)
  A Bayesian decision-theoretic alternative to ttandard multiple-comparisons adjustments in clinical trials
• Fox, Emily (Duke University, USA)
  Bayesian nonparametric time series models for complex dynamical phenomena.
• Huszar, Ferenc (University of Cambridge, UK)
  Bayesian kernel machines: the third way of going nonparametric.
• Geneletti, Sara (London School of Economics, UK)
  Uncovering selection bias in case?control studies using Bayesian poststratification.
• Ghosh, Sujit (North Carolina State University, USA)
  Bayesian shape restricted regression with multivariate Bernstein polynomials.
• Gonçalves, Flavio (University of Warwick, UK)
  Exact Simulation and Bayesian Inference for Jump-Diffusion Processes
• Guha, Subharup (University of Missouri, USA)
  Posterior simulation in countable mixture models for large datasets.
• Hahn, Richard (Duke University, USA)
  Sparse partial factor regression.
• Hans, Chris (The Ohio State University, USA)
  Penalized regression via orthant normal priors
• Henao, Ricardo (Danmarks Tekniske Universitet and Københavns Universitet, Denmark)
  Learning structure in directed acyclic graphs with latent variables
• Holmes, Christopher (University of Oxford, UK)
  Bayesian computation on graphics cards.
• Jensen, Shane (The Wharton School, USA)
  Bayesian modeling of the evolutionary escape response of HIV.
• Kolossiatis, Michalis (Cyprus University of Technology, Cyprus)
  Modelling via normalisation for Bayesian nonparametric inference.
• Krnjajic, Milovan (National University of Ireland, Ireland)
  Bayesian flexible models for censored data.
• Lacoste-Julien, Simon (University of Cambridge, UK)
  Approximate inference for the loss-calibrated Bayesian
• Lenarcic, Alan (Harvard University, USA)
  A Bayesian application of lasso in covariance network selection.
• Manolopoulou, Ioanna (Duke University, USA)
  Dynamic spatial modelling in inhomogeneous force fields.
• McCormick, Tyler (Columbia University, USA)
  Latent structure models for social networks using aggregated relational data.
• Miguez, Joaquín (Universidad Carlos III, Spain)
  Solving a class of global optimization problems by way of Bayesian estimation methods.
• Mohamed, Shakir (University of Cambridge, UK)
  Sparse exponential family latent variable models.
• Nicholls, Geoff (Oxford University, UK)
  Bayesian inference for a partial order from random linear extensions.
• Paciorek, Chris (University of California, Berkeley, USA)
  Flexible spatial latent variable modeling for combining information sources while accounting for systematic errors in proxies.
• Parnell, Andrew (University College Dublin, Ireland)
  Fast joint posterior modelling through marginal posterior mixtures.
• Pati, Debdeep (Duke University, USA)
  Bayesian geostatistical modeling with informative sampling locations
• Pillai, Natesh (University of Warwick, UK)
  MCMC in high dimensions: A new perspective
• Quirós, Alicia (Universidad Rey Juan Carlos, Spain)
  Assessing the fit of regression models for multiple imputation.
• Rancoita, Paola (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Italy)
  Bayesian integrated genomics,
• Savelieva, Marina (Novartis, Switzerland)
  Bayesian approach to Population PKPD Modelling: advantages and drawbacks.
• Scott, James (University of Texas at Austin, USA)
  Robust Bayesian shrinkage in sparse signal-extraction problems.
• Taddy, Matt (University of Chicago, USA)
  Dynamic point process podeling with a DDP.
• Teh, Yee-Whye (University College London, UK)
  Hierarchical Bayesian nonparametric models forlanguage and text.
• Wang, Jing (University of Michigan, USA)
  Approximate MCMC simulation from doubly-intractable distributions.
• Wauthier, Fabian (University of California, Berkeley, USA)
  Sparse process classification via the Gaussian copula.
• Yau, Christopher (University of Oxford, UK)
  Decision theoretic Bayesian nonparametric inference for the molecular characterisation and stratification of colorectal cancer using genome-wide microarrays.

Tutorials

As in previous editions, the conference was preceded by a set of tutorials. These were organized by ISBA and, in alphabetical order of the speaker were:

• Inoue, Lurdes (University of Washington, USA)
  A quick tour to the principles and approaches of decision theory.
• Mena, Ramsés (Universidad Nacional Autonóma de México, Mexico)
  Some topics on Bayesian nonparametric mixture models.
• Petrone, Sonia (Università Bocconi, Italia)
  Introduction to Bayesian inference.

Programme Schedule

Contributed papers

Contributed papers were presented in poster form in the plenary evening sessions which have become an identity sign of the Valencia meetings. There were be five poster sessions, every evening from June 3rd through June 7th.

Valencia 9 Proceedings

All Bayesians are warmly encouraged to submit in writing, by June 28th contributions to the discussion of one or more of the 24 papers. These should be prepared using the Valencia 9 macros, which may be downloaded using this link: V9Macros.zip

if published, they are eligible for the Lindley Prize.

Valencia Mailing List

If you are interested in Bayesian statistics, and would like to be advised of what is going on in the field, please click here
[Valencia Mailing List]

The Valencia Mailing List will soon be merged with the ISBA Mailing List, and managed by ISBA.

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Valencia International Meetings on Bayesian Statistics.
Departamento de Estadística e I.O., Facultad de Matemáticas,
Universitat de València, 46100-Burjassot, Valencia, Spain.