Bayesian Statistics 9 covers the latest and last of the Valencia International Meetings on Bayesian Statistics. These proceedings include twenty-three edited and refereed paper followed by extensive and in-depth discussion, offering a wide perspective on the developments in Bayesian statistics over the last four years.
Bayesian Statistics 9 covers the latest and last of the Valencia International Meetings on Bayesian Statistics. These proceedings include twenty-three edited and refereed paper followed by extensive and in-depth discussion, offering a wide perspective on the developments in Bayesian statistics over the last four years.
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.
Inhaltsangabe
* 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
* 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
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