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The Bayesian choice a decision-theoretic motivation

Winner of the 2004 DeGroot Prize This paperback edition, a reprint of the 2001 edition, is a graduate-level textbook that introduces Bayesian statistics and decision theory. It covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration including Gibbs sampling, and other MCMC techniques. It was awarded the 2004 DeGroot Prize by the International Society for Bayesian Analysis (ISBA) for setting "a new standard for modern textbooks dealing with Bayesian methods, especially those using MCMC techniques, and that it is a worthy successor to DeGroot's and Berger's earlier texts". Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at the Universit Paris Dauphine, and Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris. In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has written four other books, including Monte Carlo Statistical Method (Springer 2004) with George Casella and Bayesian Core (Springer 2007) with Jean-Michel Marin. He has served or is serving as associate editor for the Annals of Statistics, Bayesian Analysis, the Journal of the American Statistical Association, Statistical Science, and Sankhya. and is editor of the Journal of the Royal Statistical Society (Series B) from 20062009. He is a fellow of the Institute of Mathematical Statistics, and received the 1995 Young Statistician Award of the Socit de Statistique de Paris. Review of the second edition: "The text reads fluently and beautifully throughout, with light, good-humoured touches that warm the reader without being intrusive. There are many examples and exercises, some of which draw out the essence of work of other authors. Only a few displays and equations have numbers attached. This is an extremely fine, exceptional text of the highest quality." (ISI Short Book Reviews).

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  • "Analyse statistique bayésienne"
  • "L'analyse statistique baysienne"

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description

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  • "This graduate-level textbook presents an introduction to Bayesian statistics and decision theory. Its scope covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, hierarchical and empirical Bayes modeling, Monte Carlo integration, and Gibbs sampling. It is the translation of a successful French text. In the translation to the English edition, the author has taken the opportunity to add and update material, and to include many problems and exercises for students. From reviews of the French edition: I strongly encourage everyone teaching Bayesian decision theory to use (this) as the main textbook. Journal of the American Statisical Association On the whole, the book serves its purpose admirably. Journal of the Royal Statistical Society."
  • "Winner of the 2004 DeGroot Prize This paperback edition, a reprint of the 2001 edition, is a graduate-level textbook that introduces Bayesian statistics and decision theory. It covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration including Gibbs sampling, and other MCMC techniques. It was awarded the 2004 DeGroot Prize by the International Society for Bayesian Analysis (ISBA) for setting "a new standard for modern textbooks dealing with Bayesian methods, especially those using MCMC techniques, and that it is a worthy successor to DeGroot's and Berger's earlier texts". Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at the Université Paris Dauphine, and Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris. In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has written four other books, including Monte Carlo Statistical Method (Springer 2004) with George Casella and Bayesian Core (Springer 2007) with Jean-Michel Marin. He has served or is serving as associate editor for the Annals of Statistics, Bayesian Analysis, the Journal of the American Statistical Association, Statistical Science, and Sankhya. and is editor of the Journal of the Royal Statistical Society (Series B) from 2006-2009. He is a fellow of the Institute of Mathematical Statistics, and received the 1995 Young Statistician Award of the Société de Statistique de Paris. Review of the second edition: "The text reads fluently and beautifully throughout, with light, good-humoured touches that warm the reader without being intrusive. There are many examples and exercises, some of which draw out the essence of work of other authors. Only a few displays and equations have numbers attached. This is an extremely fine, exceptional text of the highest quality." (ISI Short Book Reviews)."
  • "Winner of the 2004 DeGroot Prize This paperback edition, a reprint of the 2001 edition, is a graduate-level textbook that introduces Bayesian statistics and decision theory. It covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration including Gibbs sampling, and other MCMC techniques. It was awarded the 2004 DeGroot Prize by the International Society for Bayesian Analysis (ISBA) for setting "a new standard for modern textbooks dealing with Bayesian methods, especially those using MCMC techniques, and that it is a worthy successor to DeGroot's and Berger's earlier texts". Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at the Universit Paris Dauphine, and Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris. In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has written four other books, including Monte Carlo Statistical Method (Springer 2004) with George Casella and Bayesian Core (Springer 2007) with Jean-Michel Marin. He has served or is serving as associate editor for the Annals of Statistics, Bayesian Analysis, the Journal of the American Statistical Association, Statistical Science, and Sankhya. and is editor of the Journal of the Royal Statistical Society (Series B) from 20062009. He is a fellow of the Institute of Mathematical Statistics, and received the 1995 Young Statistician Award of the Socit de Statistique de Paris. Review of the second edition: "The text reads fluently and beautifully throughout, with light, good-humoured touches that warm the reader without being intrusive. There are many examples and exercises, some of which draw out the essence of work of other authors. Only a few displays and equations have numbers attached. This is an extremely fine, exceptional text of the highest quality." (ISI Short Book Reviews)."@en
  • "Decision-theoretic foundations of statistical inference; From prior information to prior distributions; Bayesian point estimation; Tests and confidence regions; Admissibility and complete classes; Invariance, haar measures, and equivariant estimators; Hierarchical and empirical bayes extensions; Bayesian calculations; A defense of the bayesian choice; Appendix A: usual probability distributions; Appendix b: usual pseudorandom generators; Notation; References; Author index; Subject index."
  • "This graduate-level textbook, now in paperback, presents an introduction to Bayesian statistics and decision theory. Its scope covers both the basic ideas of statistical theory and some modern and advanced topics of Bayesian statistics."
  • "This edition introduces Bayesian statistics and decision theory and covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics."

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  • "Lehrbuch"@en
  • "Lehrbuch"
  • "Leermiddelen (vorm)"
  • "Llibres electrònics"
  • "Electronic books"@en
  • "Electronic books"

name

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  • "The Bayesian choice from decision theoretic foundations to computational implementation"
  • "The bayesian choice : a decision-theoretic motivation"
  • "The Bayesian Choice A Decision-Theoretic Motivation"
  • "The Bayesian choice : from decision-theoretic foundations to computational implementation"
  • "L'analyse statistique bayésienne"
  • "The Bayesian Choice a Decision-Theoretic Motivation"
  • "The Bayesian choice a decision-theoretic motivation"@en
  • "The Bayesian Choice : From Decision-Theoretic Foundations to Computational Implementation"
  • "The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation"@en
  • "The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation"
  • "The Bayesian choice : a decision-theoretic motivation"@en
  • "The Bayesian choice : a decision-theoretic motivation"
  • "The Bayesian choice : from decision-theoretic motivations to computational implementation"
  • "L'analyse statistique bayesienne"
  • "The Bayesian choice: from decision-theoretic foundations to computational implementation"
  • "The bayesian choice : From decision theoretic foundations to computational implementation"
  • "Analyse statistique bayésienne"@en
  • "L'analyse statistique baysienne"
  • "The bayesian choice : from decision-theoretic foundations to computational implementation"
  • "The Bayesian choice from decision-theoretic foundations to computational implementation"
  • "The Bayesian choice from decision-theoretic foundations to computational implementation"@en
  • "The Bayesian Choice"

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