Bayesian Statistics 4

Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991

Inbunden, Engelska, 1992

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Beskrivning

The Valencia International Meetings on Bayesian Statistics, held every four years, provide the forum for researchers to come together to present and discuss frontier developments in the field. The resulting Proceedings provide a definitive, up-to-date overview encompassing a wide range of theoretical and applied research. This fourth Proceedings is no exception. In particular, it reflects a growing emphasis on computational issues, concerned with making Bayesian methods routinely available to applied practitoners, both statisticians and specialists in other subject-matter, whose work depends on careful quantification of uncertainties. This book contains 30 invited papers by leading authorities, and 33 refereed contributed papers, selected from over 150 presented.

Produktinformation

Utgivningsdatum:1992-08-13
Mått:163 x 239 x 54 mm
Vikt:1 462 g
Format:Inbunden
Språk:Engelska
Antal sidor:876
Förlag:Clarendon Press
ISBN:9780198522669

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik

Innehållsförteckning

I. Invited Papers (with discussion): D.V. Lindley: Is our view of Bayesian statistics too narrow?; M.J. Bayarri & M.H. DeGroot: A "bad" view of weighted distributions and selection models; J.O. Berger & J.M. Bernardo: On the development of reference priors; J.M. Bernardo & F.J. Girón: Robust sequential prediction from non-random samples: The election night forecasting case; D.A. Berry, M.C. Wolff & D. Sack: Public health decision making: a sequential vaccine trial; P.J. Brown & T. Mäkeläinen: Regression, sequenced measurements and coherent calibration; A.P. Dawid: Prequential analysis, stochastic complexity and Bayesian inference; J.-P. Florens, M. Mouchart & J.-M. Rolin: Bayesian analysis of mixtures: Some results on exact estimability and identification; A.E. Gelfand, D.K. Dey & H. Chang: Model determination using predictive distributions, with implementation via sampling-based methods; J. Geweke: Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments; J.K. Ghosh & R. Mukerjee: Non-informative priors; P.K. Goel, C.M. Gulati & M.H. DeGroot: Optimal stopping for a non-communicating team; S.E. Hills & A.F.M. Smith: Parameterization issues in Bayesian inference; J. Hodges: Who knows what alternative lurks in the hearts of significance tests?; J.B.Kadane & D.A. Schum: Opinions in dispute: the Sacco-Vanzetti case; R.E. Kass & E.H. Slate: Reparameterization and diagnostics of posterior non-normality; X.-L. Meng & D.B. Rubin: Recent extensions to the EM algorithm; C.N. Morris & S.L. Normand: Hierarchical models for combining information and for meta-analyses; A. O'Hagan: Some Bayesian numerical analysis; D. Pena & G.C. Tiao: Bayesian robustness functions for linear models; A. Racine-Poon: SAGA: Sample assisted graphical analysis; R.M. Royall: The elusive concept of statistical evidence; M.J. Schervish: Bayesian analysis of linear models; N.D. Singpurwalla & S.P. Wilson: Warranties; D.J. Spiegelhalter & R.G. Cowell: Learning in probabilistic expert systems; I. Verdinelli: Advances in Bayesian experimental design; L. Wasserman: Recent methodological advances in robust Bayesian inference; M. West: Modelling with mixtures; R.L. Wolpert & W.J. Warren-Hicks: Bayesian hierarchical logistic models for combining field and laboratory survival data; J.V. Zidek & S. Weerahandi: Bayesian predictive inference for samples from smooth processes; J.F. Angers: B.P. Carlin & N.G. Polson: Monte Carlo Bayesian methods for discrete regression models and categorical time series; G. Consonni & P. Veronese: Bayes factors for linear models and improper priors; R.G. Cowell: BAIES - A probabilistic expert system shell with qualitative and quantitative learning; P. Dellaportas & D.E. Wright: A numerical integration strategy in Bayesian analysis; M. Farrow & M. Goldstein: Reconciling costs and benefits in experimental design; S. French, R.M. Cooke & F. Voght; The use of expert judgement in the context of a postulated mathematical model; A. Gelman & D.B. Rubin: A single series from the Gibbs sampler provides a false sense of security; A. Gilio: Co-Coherence and extension of conditional probabilities; W.R. Gilks: Derivative-free adaptive rejection sampling for Gibbs sampling; F.J. Girón, L. Martínez & C. Morcillo: A Bayesian justification for the analysis of residuals and influence measures; M.A. G^"omez-Villegas & P. Maín: The influence of prior and likelihood tail behaviour on the posterior distribution; E. Gutiérrez-Pena: Expected logarithmic divergence for exponential families; T.Z. Irony, C.A.B. Pereira & R.E. Barlow: Bayesian models for quality assurance; P.W. Jones: Multiobjective Bayesian bandits; M.B. Mendel: Bayesian parametric models for lifetimes; E. Moreno & L.R. Pericchi: Bands of probability measures: A robust Bayesian analysis; G. Parmigiani & N.G. Polson: Bayesian design for random walk barriers; M.E. Pérez & L.R. Pericchi: Analysis of multistage survey as a Bayesian hierarchical model; L.I. Pettit: Bayes factors and the effect of individual observations on the Box-Cox transformation; C.M. Queen & J.Q. Smith: Dynamic graphical models; J.M. Quintana: Optimal portfolios of forward currency contracts; A.E. Raftery & S.M. Lewis: How many iterations in the Gibbs sampler?; G.O. Roberts: Convergence diagnostics of the Gibbs sampler; S. Sivaganesan: An evaluation of robustness in binomial empirical Bayes testing; K. Sølna: Incorporating prior knowledge in a Markov point process model; F. Spizzichino: Reliability decisiion problems under conditions of ageing; D.A. Stephens & P. Dellaportas: Bayesian analysis of generalised linear models with covariate measurement error; W.E. Strawderman: The James-Stein estimator as an empirical Bayes estimator for an arbitrary location family; T.J. Sweeting: On asymptotic posterior normality in the multiparameter case; A. Thomas, D.J. Spiegelhalter & W.R. Gilks: BUGS: a program to perform Bayesian inference using Gibbs sampling; A.J. van der Merwe & C.A. van der Merwe: Empirical and hierarchical Bayes estimation in multivariate regression models; D.A. Wooff: [B/D] Works.