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2007. Bayesian Statistics 8, edited by J. M. Bernando, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West

Bernardo, J. M. 'Bayesian Statistics 8', published in 2007 in Great Britain in hardback, 678pp, ISBN 9780199214655. Condition: Brand new, unread copy. Price: £19.99, not including post and packing, which is Amazon UK's standard charge (currently £2.80 for UK buyers, more for overseas customers)
2007, Oxford University Press (Oxford Science Publications), hbk
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  • 2007. Bayesian Statistics 8 [top]
    Edited by J. M. Bernando, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West
    Published in 2007 in Great Britain in hardback, 678pp, ISBN 9780199214655

About this book: The 8th Valencia International Meeting on Bayesian Statistics was held in Benidorm (Alicante, Spain), 150 kilometres south of Valencia, from June 2nd to June 6th, 2006. These proceedings contain the 20 invited papers with their discussions and synopses of 19 contributed papers (of which five were presented orally and 14 as posters). The papers cover a broad range of topics: Foundational Issues in Statistics (several authors look at this); Disciplinary interface foundations are investigated in two papers; research in Bayesian non-parametrics is evident throughout the proceedings: several papers focus on extending and applying variants of Dirichlet process models and mixtures; flexible models for Bayesian non-parametric regression and function fitting are the primary focus of two papers; and the growth and development of objective Bayesian methods in the last several years is reflected in several papers. Some of the papers look at theory and methods for model assessment and testing; whilst others are concerned primarily with computational questions. Biomedical applications of Bayesian methods continue to represent a major area of success and growth of more realistic, complex statistical modelling. Bayesian research and applications in spatial statistics have expanded substantially over the last decade, and several authors address aspects of this. Social and policy sciences employ Bayesian methods and several papers look at this -population survey sampling (Little, Zheng)

I. INVITED PAPERS (with discussion)
-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
-Chakrabarti, A. and Ghosh, J. K.: 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., Griths, T. L. and Sollich, P.: Bayesian Nonparametric Latent Feature Models
-Giron, 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
-Muller, 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
-Almeida, C. and Mouchart, M.: Bayesian Encompassing Specification Test Under Not Completely Known Partial Observability
-Bernardo, J. M. and Perez, S.: Comparing Normal Means: New Methods for an Old Problem
Cano, J. A., Kessler, M. and Salmeron, 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
-Dukic, V. and Dignam, J.: Hierarchical Multiresolution Hazard Model for Breast Cancer Recurrence
-Hutter, M.: Bayesian Regression of Piecewise Constant Functions
-Jirsa, L., 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
-Mller, 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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