Bayesian Statistics 8: Hardback: J.M. Bernardo
- Oxford University Press

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A wide perspective of developments in Bayesian statistics over the last four years

An indispensable reference to all statisticians

Broad range of applications and models, including genetics, computer vision and computation

Authoritative reviews

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.

Readership: An indispensable reference to all statisticians.

Edited by J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith, and M. West

Contributors: Bishop, C. M. and Lasserre, J. Brooks, S. P., Manolopoulou, I. and Emerson, B. C. Ghosh, J. K. and Chakrabarti, A. Clyde, M. A. and Wolpert, R. L. Del Moral, P., Doucet, A. and Jasra, A. Gamerman, D., Salazar, E. and Reis, E. A. Gelfand, A. E., Guindani, M. and Petrone, S.

Review(s) 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

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

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