Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting by Jose M. BernardoBayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting by Jose M. Bernardo

Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting

EditorJose M. Bernardo, James O. Berger, A. P. Dawid

Hardcover | August 15, 1999

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The Valencia International Meetings on Bayesian Statistics, held every four years, provide the main forum for researchers in the area to come together to present and discuss frontier developments in the field. The resulting Proceedings provide a definitive, up-to-date overview encompassing awide range of theoretical and applied research. This sixth Proceedings is no exception, and will be an indispensable reference to all statisticians.
Jose M. Bernardo is at Universitat de Valencia. James O. Berger is at Duke University.
Title:Bayesian Statistics 6: Proceedings of the Sixth Valencia International MeetingFormat:HardcoverPublished:August 15, 1999Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198504853

ISBN - 13:9780198504856


Table of Contents

I. INVITED PAPERS (With discussion)Bayesian Inference on Latent Structure in Time SeriesInformation Theory and the Risk of Bayes ProceduresQuantifying Surprise in the Data and Model VerificationBayesian Methods in the Atmospheric SciencesNested Hypothesis Testing: The Bayesian Reference CriterionBayesian Models for Spatially Correlated Disease and Exposure DataBayesian Model Averaging and Model Search StrategiesHierarchical Models for DNA Profiling Using Heterogeneous DatabasesOn the Dangers of Modelling Through Continuous Distributions: A Bayesian PerspectiveBayesian Methods in Signal and Image ProcessingFunctional Magnetic Resonance Imaging and Spatio-Temporal InferenceSimulation Methods for Model Criticism and Robustness AnalysisExact Sampling for Bayesian Inference: Towards General Purpose AlgorithmsSpatial Regression for Marked Point ProcessesBayesian Model Choice: What and Why?Another Look at Conditionally Gaussian Markov Random FieldsSimulated Sintering: Markov Chain Monte Carlo With Spaces of Varying DimensionsMarkov Chain Monte Carlo Convergence Diagnostics: A ReviewIssues in Service Quality ModellingSimulation-Based Optimal DesignRegression and Classification Using Gaussian Process PriorsUncertainy Analysis and other Inference Tools for Complex Computer CodesDecision Models in Screening for Breast CancerTime-Varying Covariances: a Factor Stochastic Volatility ApproachOld and Recent Results on the Relationship Between Predictive Inference and Statistical Modelling either in Nonparametric or Parametric FormBayesian and Frequentist Approaches to Parametric Predictive InferenceInference-Robust Institutional Comparisons: A Case Study of School Examination ResultsComputationally Efficient Methods for Selecting Among Mixtures of Graphical ModelsSpatial Dependence and Errors-in-Variables in Environmental EpidemiologyRobustifying Bayesian ProceduresII. CONTRIBUTED PAPERSPearson Type II Errors-in-Variables ModelsBayesian Analysis of Animal Abundance Data via MCMCConvergence Assessment for Reversible Jump MCMC SimulationsFixed-Lag Smoothing using Sequential Importance SamplingThe Nile Revisited: Changepoint Analysis with AutocorrelationNon-Stationary Spatial ModellingBayesian Wavelet Analysis with a Model Complexity PriorBayesian Analysis of Cepheid Variable DataA Bayesian Analysis of Stochastic Unit Root ModelsOptimal Design for Quantal Bioassay via Monte Carlo MethodsBayesian Estimation of a Location Parameter Using the Haar BasisOn the Different Structures of Posterior Distributions with Respect to the Prior DistributionA Bayesian Proposal for the Analysis of Stationary and Nonstationary AR(1) Time SeriesBayes Sequential Decision Theory in Clinical TrialsSimplifying Complex Designs: Bayes Linear Experimental Design for Grouped Multivariate Exchangeable systemsExtremes of Mixed Environmental ProcessesGraphical Diagnostics for the Bayes Linear Analysis of Hierarchical Linear Models with Applications to Educational Data