Bayesian Statistics 9

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

State of the art Bayesian Statistics
New important applications
Authoritative reviews
Illuminating discussions
Broad international basis

Bayesian Statistics 9

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

Description

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 authors(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, high lighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as well as the corresponding growth and vitality of core theory and methodology.

The Valencia 9 invited papers cover a broad range of topics, including 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.

Bayesian Statistics 9

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

1:Integrated Objective Bayesian Estimation and Hypothesis Testing, J. M. Bernardo
2:Dynamic Stock Selection Strategies: A Structured Factor Model Framework, C. M. Carvalho, H. F. Lopes, O. Aguilar
3:Free Energy Sequential Monte Carlo, Application to Mixture Modelling, Chopin, N. and Jacob, P.
4:Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs, Consonni G. and La Rocca, L.
5:Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels, Dunson, D. B. and Bhattacharya, A.
6:Bayesian Variable Selection for Random Intercept Modeling of Gaussian and non-Gaussian Data., Frühwirth-Schnatter, S. and Wagner, H.
7:External Bayesian Analysis for Computer Simulators, Goldstein, M.
8:Optimization Under Unknown Constraints, Gramacy, R. B. and Lee, H. K. H.
9:Using TPA for Bayesian Inference, Huber, M. and Schott, S.
10:Nonparametric Bayesian Networks, Ickstadt, K., Bornkamp, B., Grzegorczyk, M., Wiecorek, J., Sherriff, M. R., Grecco, H. E. and Zamir, E.
11:Particle Learning for Sequential Bayesian Computation, Lopes, H. F., Carvalho, C. M., Johannes, M. S. and Polson, N. G.
12:Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky, Loredo, T. J.
13:Association Tests that Accommodate Genotyping Uncertainty, Louis, T. A., Carvalho, B. S., Fallin, M. D., Irizarryi, R. A., Li, Q. and Ruczinski, I.
14:Bayesian Methods in Pharmacovigilance, Madigan, D., Ryan, P., Simpson, S. and Zorych, I.
15:Approximating Max-Sum-Product Problems using Multiplicative Error Bounds, Meek, C. and Wexler, Y.
16:What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?, Meng, X.-L.
17:Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction, Polson, N. G. and Scott, J. G.
18:Bayesian Models for Sparse Regression Analysis of High Dimensional Data, Richardson, S., Bottolo, L. and Rosenthal, J. S.
19:Transparent Parametrizations of Models for Potential Outcomes, Richardson, T. S., Evans, R. J. and Robins, J. M.
20:Modelling Multivariate Counts Varying Continuously in Space, Schmidt, A. M. and Rodríguez, M. A.
21:Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models, Tebaldi, C., Sansó, B. and Smith, R. L.
22:Bayesian Models for Variable Selection that Incorporate Biological Information, Vannucci, M. and Stingo, F. C.
23:Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology, Wilkinson, D. J.

Bayesian Statistics 9

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

Author Information

Edited by José M. Bernardo, Universitat de València, M. J. Bayarri, Universitat de València, James O. Berger, Duke University, A. P. Dawid, University of Cambridge, David Heckerman, Microsoft Research, Adrian F. M. Smith, UK Department of Business, Innovation and Skills, and Mike West, Duke University

M. J. Bayarri is Professor of Statistics at Universitat de València. J. M. Bernardo is Professor of Statistics at Universitat de València. 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.

Contributors:

Aguilar, O.
Bernardo, J. M.
Bhattacharya, A.
Bornkamp, B.
Bottolo, L.
Carvalho, B. S.
Carvalho, C. M.
Chopin, N.
Consonni, G.
Dunson, D. B.
Evans, R. J.
Fallin, M. D.
Frühwirth-Schnatter, S.
Goldstein, M.
Gramacy, R. B.
Grecco, H. E.
Grzegorczyk, M.
Huber, M.
Ickstadt, K.
Irizarryi, R. A.
Jacob, P.
Johannes, M. S.
La Rocca, L.
Lee, H. K. H.
Li, Q.
Lopes, H. F.
Loredo, T. J.
Louis, T. A.
Madigan, D.
Meek, C.
Meng, X.-L.
Polson, N. G.
Richardson, S.
Richardson, T. S.
Robins, J. M.
Rodríguez, M. A.
Rosenthal, J. S.
Ruczinski, I.
Ryan, P.
Schott, S.
Schmidt, A. M.
Scott, J. G.
Sansó, B.
Sherriff, M. R.
Simpson, S.
Smith, R. L.
Stingo, F. C.
Tebaldi, C.,
Vannucci, M.
Wagner, H.
Wexler, Y.
Wiecorek, J.
Wilkinson, D. J.
Zamir, E.
Zorych, I.

Bayesian Statistics 9

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

Reviews and Awards

Review from previous edition Review from previous edition ... this book presents a uniquely excellent overview of some of the most relevant and pressing current issues underlying research in Bayesian statistics today. That such a definitive and all-encompassing presentation of a wide range of current concerns is fused in a single volume is by any measure its primary attraction. The format has additional appeal given the conference organizers' well-judged decision to encourage contributed discussion for the invited papers. This is particularly useful in bringing the most salient points to the forefront of the readers' attention. - Journal of the Royal Statistical Society

This volume will be of most use for the research-orientated investigator, or for a casual reader of Bayesian literature, both as stimulating to read and as a useful reference text. - Journal of the Royal Statistical Society

... this collection provides an excellent overview of current research in Bayesian statistics ... Given the high quality of most papers in this volume, and the range of interesting applications, this is a must for academic libraries. I would advise researchers in Statistics, OR, and related fields to have a look at the volume, as it provides a fast overview of recent developments in Bayesian statistics. Some of the applications might also provide useful examples for teaching statistics at the postgraduate level. - Journal of the Operational Research Society