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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

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