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