Bayesian Statistics 9
Published on 6. October 2011
Book
Hardback
718 pages
978-0-19-969458-7 (ISBN)
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.
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.
Reviews / Votes
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 *More details
Language
English
Place of publication
Oxford
United Kingdom
Target group
College/higher education
Professional and scholarly
Suitable for statisticians and graduate students who want to keep up with new developments in the field and for scientists who want to learn about solutions to problems in their field not supplied by conventional statistics.
Illustrations
151 black and white line drawings
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 43 mm
Weight
1230 gr
ISBN-13
978-0-19-969458-7 (9780199694587)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
M. J. Bayarri is Professor of Statistics at Universitat de Valencia.
J. M. Bernardo is Professor of Statistics at Universitat de Valencia.
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.
J. M. Bernardo is Professor of Statistics at Universitat de Valencia.
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.
Content
1. Integrated Objective Bayesian Estimation and Hypothesis Testing ; 2. Dynamic Stock Selection Strategies: A Structured Factor Model Framework ; 3. Free Energy Sequential Monte Carlo, Application to Mixture Modelling ; 4. Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs ; 5. Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels ; 6. Bayesian Variable Selection for Random Intercept Modeling of Gaussian and non-Gaussian Data. ; 7. External Bayesian Analysis for Computer Simulators ; 8. Optimization Under Unknown Constraints ; 9. Using TPA for Bayesian Inference ; 10. Nonparametric Bayesian Networks ; 11. Particle Learning for Sequential Bayesian Computation ; 12. Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky ; 13. Association Tests that Accommodate Genotyping Uncertainty ; 14. Bayesian Methods in Pharmacovigilance ; 15. Approximating Max-Sum-Product Problems using Multiplicative Error Bounds ; 16. What's the H in H-likelihood: A Holy Grail or an Achilles' Heel? ; 17. Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction ; 18. Bayesian Models for Sparse Regression Analysis of High Dimensional Data ; 19. Transparent Parametrizations of Models for Potential Outcomes ; 20. Modelling Multivariate Counts Varying Continuously in Space ; 21. Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models ; 22. Bayesian Models for Variable Selection that Incorporate Biological Information ; 23. Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology