About the Book
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 discuss 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 31 contributed papers is no exception, and
will be an indispensable reference to all statisticians.
Table of Contents:
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
Gutiérrez-Peña, 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 Sköld, M.: Non-Centered Parameterisations for Hierarchical Models and Data Augmentation
Peña, D., Rodríguez, 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
Ausín, 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 Juárez, 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 Pólya-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., Kárn´y, M. and Quinn, A.: Lymphoscintigraphy of Upper Limbs: A Bayesian Framework
Girón, F. J., Martínez, 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
Rodríguez, A., Álvarez, G. and Sansó, 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., Martín, J., Ríos-Insua, D. and Moreno-Díaz, 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
About the Author :
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
Review :
... 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
