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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).
The 23 Valencia 9 invited papers, in the tradition of earlier editions, cover a broad range of topics. This includes foundational and core theoretical issues in statistics, the continued development of new and refined computational methods for complex Bayesian modelling, substantive applications of flexible Bayesian modelling, and new developments in the theory and methodology of graphical modelling. They also describe advances in methodology for specific applied fields, including financial econometrics and portfolio decision making, public policy applications for drug surveillance, studies in the physical and environmental sciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.
1: J. M. Bernardo: Integrated Objective Bayesian Estimation and Hypothesis Testing
2: C. M. Carvalho, H. F. Lopes, O. Aguilar: Dynamic Stock Selection Strategies: A Structured Factor Model Framework
3: Chopin, N. and Jacob, P.: Free Energy Sequential Monte Carlo, Application to Mixture Modelling
4: Consonni G. and La Rocca, L.: Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs
5: Dunson, D. B. and Bhattacharya, A.: Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels
6: Fr#hwirth-Schnatter, S. and Wagner, H.: Bayesian Variable Selection for Random Intercept Modeling of Gaussian and non-Gaussian Data.
7: Goldstein, M.: External Bayesian Analysis for Computer Simulators
8: Gramacy, R. B. and Lee, H. K. H.: Optimization Under Unknown Constraints
9: Huber, M. and Schott, S.: Using TPA for Bayesian Inference
10: Ickstadt, K., Bornkamp, B., Grzegorczyk, M., Wiecorek, J., Sherriff, M. R., Grecco, H. E. and Zamir, E.: Nonparametric Bayesian Networks
11: Lopes, H. F., Carvalho, C. M., Johannes, M. S. and Polson, N. G.: Particle Learning for Sequential Bayesian Computation
12: Loredo, T. J.: Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky
13: Louis, T. A., Carvalho, B. S., Fallin, M. D., Irizarryi, R. A., Li, Q. and Ruczinski, I.: Association Tests that Accommodate Genotyping Uncertainty
14: Madigan, D., Ryan, P., Simpson, S. and Zorych, I.: Bayesian Methods in Pharmacovigilance
15: Meek, C. and Wexler, Y.: Approximating Max-Sum-Product Problems using Multiplicative Error Bounds
16: Meng, X.-L.: What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?
17: Polson, N. G. and Scott, J. G.: Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction
18: Richardson, S., Bottolo, L. and Rosenthal, J. S.: Bayesian Models for Sparse Regression Analysis of High Dimensional Data
19: Richardson, T. S., Evans, R. J. and Robins, J. M.: Transparent Parametrizations of Models for Potential Outcomes
20: Schmidt, A. M. and Rodr#guez, M. A.: Modelling Multivariate Counts Varying Continuously in Space
21: Tebaldi, C., Sans#, B. and Smith, R. L.: Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models
22: Vannucci, M. and Stingo, F. C.: Bayesian Models for Variable Selection that Incorporate Biological Information
23: Wilkinson, D. J.: Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology
M. J. Bayarri is Professor of Statistics at Universitat de Val#ncia. J. M. Bernardo is Professor of Statistics at Universitat de Val#ncia. James O. Berger is the Arts and Sciences Professor of Statistics at Duke University A. P. Dawid is Professor of Statistics at the University of Cambridge. David Heckerman is the Senior Director of the eScience Research Group for Microsoft. Sir Adrian F M Smith is the Director General of Science and Research at the UK Department of Business, Innovation and Skills. Mike West is the Arts and Sciences Professor of Statistical Science at Duke University.
