About the Book
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.
Table of Contents:
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: Frühwirth-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 Rodríguez, M. A.: Modelling Multivariate Counts Varying Continuously in Space
21: Tebaldi, C., Sansó, 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
About the Author :
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.
Review :
`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
