Bayesian Statistics 9

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JosÚ M. Bernardo, M. J. Bayarri, James O. Berger
OUP Oxford, Oct 6, 2011 - Mathematics - 706 pages
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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 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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Contents

Integrated Objective Bayesian Estimation and Hypothesis Testing
1
A Structured Factor Model Framework
69
Free Energy Sequential Monte Carlo Application to Mixture Modelling
91
Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs
119
Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels
145
Bayesian Variable Selection for Random Intercept Modelling of Gaussian and NonGaussian Data
165
External Bayesian Analysis for Computer Simulators
201
Optimization Under Unknown Constraints
229
Association Tests that Accommodate Genotyping Uncertainty
393
Bayesian Methods in Pharmacovigilance
421
Approximating MaxSumProduct Problems using Multiplicative Error Bounds
439
A Holy Grail or an Achilles Heel?
473
Sparse Bayesian Regularization and Prediction
501
Bayesian Models for Sparse Regression Analysis of High Dimensional Data
539
Transparent Parametrizations of Models for Potential Outcomes
569
Modelling Multivariate Counts Varying Continuously in Space
611

Using TPA for Bayesian Inference
257
Nonparametric Bayesian Networks
283
Particle Learning for Sequential Bayesian Computation
317
Bayesian Exploration of the Pulsating Sky
361
Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models
639
Bayesian Models for Variable Selection that Incorporate Biological Information
659
A Bayesian Approach to Systems Biology
679
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About the author (2011)

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 ofCambridge. David Heckerman is the Senior Director of the Science 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 DukeUniversity.

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