Bayesian Statistics 8

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. Covering a broad range of applications and models, including genetics, computer vision and computation, the resulting proceedings provide a definitive, up-to-date overview encompassing a wide range of theoretical and applied research. This eighth proceedings includes edited and refereed versions of 20 invited papers plus extensive and in-depth discussion along with 19 extended four page abstracts of the best presentations offering a wide perspective of the developments in Bayesian statistics over the last four years.

Table of Contents:
Bishop, C. M. and Lasserre, J.: Generative or Discriminative? Getting the Best of Both Worlds Brooks, S. P., Manolopoulou, I. and Emerson, B. C.: Assessing the Effect of Genetic Mutation - A Bayesian Framework for Determining Population History from DNA Sequence Data Ghosh, J. K. and Chakrabarti, A.: Some Aspects of Bayesian Model Selection for Prediction Clyde, M. A. and Wolpert, R. L.: Nonparametric Function Estimation Using Overcomplete Dictionaries Del Moral, P., Doucet, A. and Jasra, A.: Sequential Monte Carlo for Bayesian Computation Gamerman, D., Salazar, E. and Reis, E. A.: Dynamic Gaussian Process Priors, with Applications to The Analysis of Space-time Data Gelfand, A. E., Guindani, M. and Petrone, S.: Bayesian Nonparametric Modelling for Spatial Data Using Dirichlet Processes Ghahramani, Z., Griffiths, T. L. and Sollich, P.: Bayesian Nonparametric Latent Feature Models Gir´on, F. J., Moreno, E. and Casella, G.: Objective Bayesian Analysis of Multiple Changepoints for Linear Models Holmes, C. C. and Pintore, A.: Bayesian Relaxation: Boosting, The Lasso, and other L norms Little, R. J. A. and Zheng, H.: The Bayesian Approach to the Analysis of Finite Population Surveys Merl, D. and Prado, R.: Detecting selection in DNA sequences: Bayesian Modelling and Inference Mira, A. and Baddeley, A.: Deriving Bayesian and frequentist estimators from time-invariance estimating equations: a unifying approach M¨uller, P., Parmigiani, G. and Rice, K.: FDR and Bayesian Multiple Comparisons Rules Raftery, A., Newton, M., Satagopan, J. and Krivitsky, P.: Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity. Rousseau, J.: Approximating Interval Hypothesis: p-values and Bayes Factors Schack, R.: Bayesian Probability in Quantum Mechanics Schmidler, S. C.: Fast Bayesian Shape Matching Using Geometric Algorithms Skilling, J.: Nested Sampling for Bayesian Computations Sun, D. and Berger, J. O.: Objective Bayesian Analysis for the Multivariate Normal Model CONTRIBUTED PAPERS Almeida, C. and Mouchart, M.: Bayesian Encompassing Specification Test Under Not Completely Known Partial Observability Bernardo, J. M. and P´erez, S.: Comparing Normal Means: New Methods for an Old Problem Cano, J. A., Kessler, M. and Salmer´on, D.: Integral Priors for the One Way Random Effects Model Carvalho, C. M. and West, M.: Dynamic Matrix-Variate Graphical Models Cowell, R. G., Lauritzen, S.L. and Mortera, J.: A Gamma Model for DNA Mixture Analyses Denham, R. J. and Mengersen, K.: Geographically Assisted Elicitation of Expert Opinion for Regression Models Duki´c, V. and Dignam, J.: Hierarchical Multiresolution Hazard Model for Breast Cancer Recurrence Hutter, M.: Bayesian Regression of Piecewise Constant Functions Jirsa, J., Quinn, A. and Varga, F.: Identification of Thyroid Gland Activity in Radiotherapy. Kokolakis, G. and Kouvaras, G.: Partial Convexification of Random Probability Measures Ma, H. and Carlin, B. P.: Bayesian Multivariate Areal Wombling Madrigal, A. M.: Cluster Allocation Design Networks Mertens, B. J. A.: Logistic Regression Modelling of Proteomic Mass Spectra in a Case-Control Study on Diagnosis for Colon Cancer Møller, J. and Mengersen, K.: Ergodic Averages Via Dominating Processes Perugia, M.: Bayesian Model Diagnostics Based on Artificial Autoregressive Errors Short, M. B., Higdon, D. M. and Kronberg, P. P.: Estimation of Faraday Rotation Measures of the Near Galactic Sky, Using Gaussian Process Models Spitzner, D. J.: An Asymptotic Viewpoint on High-Dimensional Bayesian Testing Wallstrom, T. C.: The Marginalization Paradox and Probability Limits Xing, E. P. and Sohn, K.-A.: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space

Review :
`Review from previous edition ... 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