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Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting als Buch
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Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting

Sprache: Englisch.
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The Valencia International Meetings on Bayesian Statistics, held every four years, provide the main forum for researchers in the area of Bayesian Statistics to come together to present and discus frontier developments in the field. The resulting proc... weiterlesen
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Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting als Buch
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Titel: Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting

ISBN: 0198526156
EAN: 9780198526155
Sprache: Englisch.
Herausgegeben von J. M. Bernardo, A. Philip Dawid
OXFORD UNIV PR

September 2003 - gebunden - 768 Seiten

Beschreibung

The Valencia International Meetings on Bayesian Statistics, held every four years, provide the main forum for researchers in the area of Bayesian Statistics to come together to present and discus frontier developments in the field. The resulting proceedings provide a definitive, up-to-date overview encompassing a wide range of theoretical and applied research. This Seventh Proceedings containing 23 invited articles and 30 contributed papers is no exception and will be an indispensable reference to all statisticians.

Inhaltsverzeichnis

Arellano-Valle, R. B., Iglesias, P. L. and Vidal I.: Bayesian Inference for Elliptical Linear Models: Conjugate Analysis and Model Comparison ; Blei, D. M., Jordan, M. I. and Ng, A. Y.: Hierarchical Bayesian Models for Applications in Information Retrieval ; Carlin, B. P. and Banerjee, S.: Hierarchical Multivariate CAR Models for Spatio- Temporally Correlated Survival Data ; Chib, S.: On Inferring Effects of Binary Treatments with Unobserved Confounders ; Chipman, H. A., George, E. I. and McCulloch, R. E.: Bayesian Treed Generalized Linear Models ; Davy, M. and Godsill, S. J.: Bayesian Harmonic Models for Musical Signal Analysis ; Dobra, A., Fienberg, S. E. and Trottini, M.: Assessing the Risk of Disclosure of Confidential Categorical Data. ; Genovese, C. and Wasserman, L: Bayesian and Frequentist Multiple Testing ... 145 ; Gutierrez-Pena, E. and Nieto-Barajas, L. E.: Nonparametric Inference for Mixed Poisson Processes ; Higdon, D., Lee, H. and Holloman, C. : Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems ; Johnson, V. E., Graves, T. L., Hamada, M. S. and Shane, C.: Reese A Hierarchical Model for Estimating the Reliability of Complex Systems ; Lauritzen, S. L.: Rasch Models with Exchangeable Rows and Columns ; Linde, A. Van Der and Osius, G.: Discrimination Based on an Odds Ratio Parameterization ; Liu, J. S., Zhang, J. L., Palumbo, M. J. and Charles, E.: Lawrence Bayesian Clustering with Variable and Transformation Selections ; Mengersen, K. L. and Robert, C. P.: Iid Sampling using Self-Avoiding Population Monte Carlo: The Pinball Sampler ; Newton, M. A., Yang H., Gorman, P., Tomlinson, I. and Roylance, R.: A Statistical Approach to Modeling Genomic Aberrations in Cancer Cells ; Papaspiliopoulos, O., Roberts, G. O. and Skold, M.: Non-Centered Parameterisations for Hierarchical Models and Data Augmentation ; Pena, D., Rodriguez, J. and Tiao, G. C.: Identifying Mixtures of Regression Equations by the SAR procedure ; Quintana, J. M., Lourdes V., Aguilar, O. and Liu, J.: Global Gambling ; Salinetti, G.: New Tools for Consistency in Bayesian Nonparametrics ; Schervish, M. J., Seidenfeld T. and Kadane, J. B.: Measures of Incoherence: How not to Gamble if you Must ; Wolpert, R. L., Ickstadt, K. and Hansen, M. B.: A Nonparametric Bayesian Approach to Inverse Problems ; Zohar, R. and Geiger, D.: A Novel Framework for Tracking Groups of Objects ; II. CONTRIBUTED PAPERS ; Ausin, M. C., Lillo, R. E., Ruggeri, F. and Wiper, M. P. : Bayesian Modeling of Hospital Bed Occupancy Times using a Mixed Generalized Erlang Distribution ; Beal, M. J. and Ghahramani, Z.: The Variational Bayesian EM Algorithm for Incomplete Data: With Application to Scoring Graphical Model Structures ; Bernardo, J. M. and Juarez, M. A.: Intrinsic Estimation ; Choy S. T. B., Chan J. S. K. and YamH. K.: Robust Analysis of Salamander Data, Generalized Linear Model with Random Effects ; Daneshkhah, A. and Smith, Jim Q.: A Relationship Between Randomised Manipulation and Parameter Independence ; Dethlefsen, C.: Markov Random Field Extensions using State Space Models ; Erosheva, E. A.: Bayesian Estimation of the Grade of Membership Model ; Esteves, L. G., Wechsler, S., Iglesias, P. L. and Pereira, A. L.: A Variant Version of the Polya-Eggenberger Urn Model ; Ferreira, A. R., West, M., Lee, H. K. H., Higdon, D. and Bi, Z.: Multi-scale Modelling of 1-D Permeability Fields ; Fraser, D. A. S., Reid, N., Wong, A. and Yi, G. Y.: Direct Bayes for Interest Parameters ; Garside, L. M. and Wilkinson, D. J.: Dynamic Lattice-Markov Spatio-Temporal Models for Environmental Data ; Gebousk'y, P., Karn'y, M. and Quinn, A.: Lymphoscintigraphy of Upper Limbs: A Bayesian Framework ; Giron, F. J., Martinez, M. L., Moreno, E. and Torres, F.: Bayesian Analysis of Matched Pairs in the Presence of Covariates ; Jamieson, L. E. and Brooks, S. P.: State Space Models for Density Dependence in Population Ecology ; Lavine, M.: A Marginal Ergodic Theorem ; Lefebvre, T., Gadeyne, K., Bruyninckx, H. and Schutter, J. D.: Exact Bayesian Inference for a Class of Nonlinear Systems with Application to Robotic Assembly ; Leucari, V. and Consonni, G.: Compatible Priors for Causal Bayesian Networks ; Mertens, B. J. A.: On the Application of Logistic Regression Modeling in Microarray Studies ; Neal, R. M.: Dens ity Modeling and Clustering Using Dirichlet Diffusion Trees ; Pettit, L. I. and Sugden, R. A.: Outl ier Robust Estimation of a Finite Population Total ; Polson, N. G. and Stroud, J. R.: Bayesian Inference f or Derivative Prices ; Rasmussen, C. E.: Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals ; Rodriguez, A., Alvarez, G. and Sanso, B.: Objective Bayesian Comparison of Laplace Samples from Geophysical Data ; Scott, S. L. and Smyth, P.: The Markov Modulated Poisson Process and Markov Poisson Cascade with Applications to Web Traffic Modeling ; Smith, E. L. and Walshaw, D.: Modelling Bivariate Extremes in a Region ; Vehtari, and Lampinen, J.: Expected Utility Estimation via Cross-Validation ; Virto, M., Martin, J., Rios-Insua, D. and Moreno-Diaz, A.: A Method for Sequential Optimization in Bayesian Analysis ; Wakefield, J. C., Zhou, C. and Self, S. G.: Modelling Gene Expression Data over Time: Curve Clustering with Informative Prior Distributions ; West, M: Bayesian Factor Regression Models in the Large p, Small n Paradigm ; Zheng, P. and Marriott, J. M.: A Bayesian Analysis of Smooth Transitions in Trend ; Tamminen, T. and Lampinen. J: Bayesian Object Matching with Hierarchical Priors and Markov Chain Monte Carlo

Portrait

Professor José M. Bernardo
Professor of Statistics, Universidad de Valencia, Spain

A. Philip Dawid
Professor of Statistics, University College London, UK
AWARDS:
2002 DeGroot Prize for a Published Book in Statistical Science (Cowell et al.)
2001 Royal Statistical Society: Guy Medal in Silver
1978 Royal Statistical Society: Guy Medal in Bronze
1977 G. W. Snedecor Award for Best Publication in Biometry


David Heckerman
Senior Researcher, Microsoft
AAAI Fellow, 2001
Association for Computing Machinery Doctoral Dissertation Award, 1991


Mike West
The Arts & Sciences Professor of Statistics & Decision Sciences
Institute of Statistics and Decision Sciences, Duke University


James O. Berger
Professor of Statistics, Duke University

Professor M.J. Bayarri
Professor of Statistics, Universidad de Valencia, Spain

Professor Adrian F.M. Smith
Principal, Queen Mary University of London

Pressestimmen

... this book presents a uniquely excellent overview of some of the most relevant and pressing current issues underlying research in Bayesian statistics today. That such a definitive and all-encompassing presentation of a wide range of current concerns is fused in a single volume is by any measure its primary attraction. The format has additional appeal given the conference organizers' well-judged decision to encourage contributed discussion for the invited papers. This is particularly useful in bringing the most salient points to the forefront of the readers' attention. Journal of the Royal Statistical Society This volume will be of most use for the research-orientated investigator, or for a casual reader of Bayesian literature, both as stimulating to read and as a useful reference text. Journal of the Royal Statistical Society ... 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 related fields 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

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