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Bayesian Statistics 9

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Overview

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.

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Product Details

  • ISBN-13: 9780199694587
  • Publisher: Oxford University Press
  • Publication date: 12/17/2011
  • Pages: 720
  • Product dimensions: 9.20 (w) x 6.10 (h) x 1.80 (d)

Meet the Author

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.

Mike West is the Arts and Sciences Professor of Statistical Science at Duke University.

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Table of Contents

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., Fruhwirth-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 Rodriguez, M. A.
21. Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models, Tebaldi, C., Sanso, 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.

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