Buch, Englisch, 718 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1230 g
Buch, Englisch, 718 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1230 g
ISBN: 978-0-19-969458-7
Verlag: ACADEMIC
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.
Zielgruppe
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
- 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
