Bayesian Data Analysis, 3rd edition (Chapman & Hall/CRC Texts in Statistical Science)

Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; Vehtari, Aki; Rubin, Donald B.

ISBN 10: 1439840954 / ISBN 13: 9781439840955

Edité par U.S.A.: Chapman and Hall/CRC, 2013

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**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

Détails bibliographiques

Titre : Bayesian Data Analysis, 3rd edition (Chapman...

Éditeur : U.S.A.: Chapman and Hall/CRC

Date d'édition : 2013

Reliure : Soft cover

Etat : New

Etat de la jaquette : New

Edition : 3rd Edition, International Edition

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Synopsis :

Book by Gelman Andrew Carlin John B Stern Hal S Dunson Dav

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.