Bayesian Statistics 9 by Jose M. BernardoBayesian Statistics 9 by Jose M. Bernardo

Bayesian Statistics 9

EditorJose M. Bernardo, M. J. Bayarri, James O. Berger

Hardcover | November 6, 2011

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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 invitedpapers 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 manyfields 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 newdevelopments 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 environmentalsciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.
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 Directo...
Title:Bayesian Statistics 9Format:HardcoverDimensions:720 pages, 9.21 × 6.14 × 0 inPublished:November 6, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199694583

ISBN - 13:9780199694587


Table of Contents

1. J. M. Bernardo: Integrated Objective Bayesian Estimation and Hypothesis Testing2. C. M. Carvalho, H. F. Lopes, O. Aguilar: Dynamic Stock Selection Strategies: A Structured Factor Model Framework3. Chopin, N. and Jacob, P.: Free Energy Sequential Monte Carlo, Application to Mixture Modelling4. Consonni G. and La Rocca, L.: Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs5. Dunson, D. B. and Bhattacharya, A.: Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels6. Fruhwirth-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 Simulators8. Gramacy, R. B. and Lee, H. K. H.: Optimization Under Unknown Constraints9. Huber, M. and Schott, S.: Using TPA for Bayesian Inference10. Ickstadt, K., Bornkamp, B., Grzegorczyk, M., Wiecorek, J., Sherriff, M. R., Grecco, H. E. and Zamir, E.: Nonparametric Bayesian Networks11. Lopes, H. F., Carvalho, C. M., Johannes, M. S. and Polson, N. G.: Particle Learning for Sequential Bayesian Computation12. Loredo, T. J.: Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky13. Louis, T. A., Carvalho, B. S., Fallin, M. D., Irizarryi, R. A., Li, Q. and Ruczinski, I.: Association Tests that Accommodate Genotyping Uncertainty14. Madigan, D., Ryan, P., Simpson, S. and Zorych, I.: Bayesian Methods in Pharmacovigilance15. Meek, C. and Wexler, Y.: Approximating Max-Sum-Product Problems using Multiplicative Error Bounds16. 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 Prediction18. Richardson, S., Bottolo, L. and Rosenthal, J. S.: Bayesian Models for Sparse Regression Analysis of High Dimensional Data19. Richardson, T. S., Evans, R. J. and Robins, J. M.: Transparent Parametrizations of Models for Potential Outcomes20. Schmidt, A. M. and Rodriguez, M. A.: Modelling Multivariate Counts Varying Continuously in Space21. Tebaldi, C., Sanso, B. and Smith, R. L.: Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models22. Vannucci, M. and Stingo, F. C.: Bayesian Models for Variable Selection that Incorporate Biological Information23. Wilkinson, D. J.: Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology

Editorial Reviews

"... 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 relatedfields 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 d 08/07/2004