Bayesian Data Analysis, Third Edition

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9781439840955: Bayesian Data Analysis, Third Edition
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Book by Gelman Andrew Carlin John B Stern Hal S Dunson Dav

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur :

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

  • Four new chapters on nonparametric modeling
  • Coverage of weakly informative priors and boundary-avoiding priors
  • Updated discussion of cross-validation and predictive information criteria
  • Improved convergence monitoring and effective sample size calculations for iterative simulation
  • Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
  • New and revised software code

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Présentation de l'éditeur :

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

  • Four new chapters on nonparametric modeling
  • Coverage of weakly informative priors and boundary-avoiding priors
  • Updated discussion of cross-validation and predictive information criteria
  • Improved convergence monitoring and effective sample size calculations for iterative simulation
  • Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
  • New and revised software code

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

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Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; Vehtari, Aki; Rubin, Donald B.
Edité par U.S.A.: Chapman and Hall/CRC (2013)
ISBN 10 : 1439840954 ISBN 13 : 9781439840955
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Description du livre U.S.A.: Chapman and Hall/CRC, 2013. Soft cover. Etat : New. Etat de la jaquette : New. International Edition. Low price guarantee! The book is the brand new international edition textbook with the different ISBN and cover design. The book main content black/white printed in full English as same as the corresponding original US edition. Fast shipments will sent out by DHL/UPS or standard post mail with tracking numbers in one to two working days after the orders confirmed. N° de réf. du vendeur ABE-4419418315127404616

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Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; Vehtari, Aki; Rubin, Donald B.
Edité par U.S.A.: Chapman and Hall/CRC (2013)
ISBN 10 : 1439840954 ISBN 13 : 9781439840955
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Description du livre U.S.A.: Chapman and Hall/CRC, 2013. Soft cover. Etat : New. Etat de la jaquette : New. 3rd Edition, International Edition. **INTERNATIONAL EDITION** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments go through via USPS/UPS/DHL with tracking numbers. Great professional textbook selling experience and expedite shipping service. N° de réf. du vendeur ABE-8693184296589653654

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Gelman, Andrew, Carlin, John B., Stern, Hal S., Dunson, David B., Vehtari, Aki, Rubin, Donald B.
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Description du livre Chapman and Hall/CRC, 2013. HRD. Etat : New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. N° de réf. du vendeur FT-9781439840955

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Description du livre Taylor & Francis Ltd, United States, 2013. Hardback. Etat : New. 3rd New edition. Language: English. Brand new Book. Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.New to the Third EditionFour new chapters on nonparametric modelingCoverage of weakly informative priors and boundary-avoiding priorsUpdated discussion of cross-validation and predictive information criteriaImproved convergence monitoring and effective sample size calculations for iterative simulationPresentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagationNew and revised software codeThe book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page. N° de réf. du vendeur AA69781439840955

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Description du livre Taylor & Francis Ltd, United States, 2013. Hardback. Etat : New. 3rd New edition. Language: English. Brand new Book. Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.New to the Third EditionFour new chapters on nonparametric modelingCoverage of weakly informative priors and boundary-avoiding priorsUpdated discussion of cross-validation and predictive information criteriaImproved convergence monitoring and effective sample size calculations for iterative simulationPresentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagationNew and revised software codeThe book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page. N° de réf. du vendeur AA69781439840955

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