9781439840955-1439840954-Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)

3.5
ISBN-13: 9781439840955
ISBN-10: 1439840954
Edition: 3
Author: Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Publication date: 2013
Publisher: Chapman and Hall/CRC
Format: Hardcover 675 pages
Category: Statistics
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Book details

ISBN-13: 9781439840955
ISBN-10: 1439840954
Edition: 3
Author: Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Publication date: 2013
Publisher: Chapman and Hall/CRC
Format: Hardcover 675 pages
Category: Statistics

Summary

Acknowledged authors Andrew Gelman , John B. Carlin , Hal S. Stern , David B. Dunson , Aki Vehtari , Donald B. Rubin wrote Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) comprising 675 pages back in 2013. Textbook and eTextbook are published under ISBN 1439840954 and 9781439840955. Since then Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) textbook received total rating of 3.5 stars and was available to sell back to BooksRun online for the top buyback price of $ 38.06 or rent at the marketplace.

Description

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.

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