Bayesian statistics 8: proceedings of the eighth Valencia International Meeting, June 2-6, 2006, Volume 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.
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Generative or Discriminative?
Some Aspects of Bayesian Model
24 other sections not shown
A. F. M. Smith A. P. Dawid algorithm applications approximation associated assume asymptotic Bayes factor Bayesian analysis Bayesian approach Bayesian inference Bayesian model BICM binary changepoint choice cladogram cluster components computation conditional consider convergence corresponding covariance defined denote density described dimensional Dirichlet process discussion equation example exponential finite frequentist gamma gene given harmonic mean harmonic mean estimator hierarchical improper priors independent integrated likelihood intrinsic priors J. M. Bernardo J. O. Berger kernel latent feature linear locations loss function marginal likelihood Markov chain matching matrix MCMC measure methods mixture models model selection multivariate nested sampling nodes nonparametric normal null objective observed obtained optimal p-value paper posterior distribution posterior probability predictive prior distribution problem procedure proposed quantum random field reference prior regression sampler Section sequence simulation space spatial specific Statist stochastic structure testing theorem updating values variance vector wavelet