Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan Author(s): John Kruschke

Difference between 2nd and 1st editions
Side by side comparison of table of contents helps to figure out the most significant changes.

Key differences between 2nd and 1st editions

  • Period between current and previous editions: 4 years (2014 vs 2010).
  • In the Second Edition J. Kruschke presents all new programs in JAGS and Stan.
  • New Chapter 3 on R Programming Language, which is used for the development of computer programs for statistics and data analysis.
  • Two former separate chapters -- Chapter 7 "Inferring a Binomial Proportion via the Metropolis Algorithm" and Chapter 8 "Inferring Two Binomial Proportions via Gibbs Sampling" -- have been consolidated into a single Chapter 7 "Markov Chain Monte Carlo". This chapter now presents new material on Markov Chain Monte Carlo (MCMC) convergence diagnostic methods.
  • Completely new Chapter 8 on Just another Gibbs sampler (JAGS), a program for investigation of Bayesian hierarchical models, written by Martyn Plumme.
  • Chapter 9 includes new material on shrinkage in hierarchical models.
  • New Chapter 10 "Model Comparison and Hierarchical Modeling" consolidated into a single chapter all the material on model comparison, which was spread across various chapters in the First edition.
  • Chapter 10 now contains all the material on model comparison, which was spread across multiple chapters before.
  • Chapter 11: new material on sampling distributions used to test hypotheses.
  • Chapter 12: new material about the region of practical equivalence (ROPE); Savage-Dickey approximations to the Bayes factor.
  • Chapter 13: new section 13.3 "Sequential Testing and the Goal of Precision".
  • New Chapter 14 on Stan, an imperative probabilistic programming language.
  • Chapter 16: new section 16.3 on metric predicted variable on two groups.
  • Chapter 18: new section 18.4 "Variable Selection".
  • Chapter 19: new examples on analysis of covariance (ANCOVA).
  • Chapter 21 includes examples of robust logistic regression.
  • New Chapter 22 "Nominal Predicted Variable" covers:
    • Softmax regression (a form of multinomial logistic regression)
    • Conditional (fixed-effects) logistic regression
    • Implementation in JAGS (Just Another Gibbs Sampler)
    • Models generalizations and variations
  • Chapter 23 has examples showing single-group and two-group analyses.
  • Chapter 25: new section 25.4 "Censored Data in JAGS".
2nd Edition of
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
eBook, 746 pages
eBook ISBN: 9780124059160
Published by: Academic Press, November 11, 2014
Book StorePrice
Available
$107.94
Hardcover, 776 pages
ISBN-10: 0124058884
ISBN-13: 9780124058880
Published by: Academic Press, November 17, 2014
Book StoreNewUsedRent
$79.32
From: $63.25
$87.70 $80.96 due: Jun 04
$76.46 due: Mar 11
$71.96 due: Feb 09
1st Edition of
Doing Bayesian Data Analysis: A Tutorial Introduction with R
eBook, 672 pages
eBook ISBN: 9780123814869
Published by: Academic Press, November 25, 2010
Book StorePrice
Available
$107.94
Hardcover, 672 pages
ISBN-10: 0123814855
ISBN-13: 9780123814852
Published by: Academic Press, November 10, 2010
Book StoreNewUsedRent
$61.99
From: $48.91

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Hardcover
  • Used 2nd edition can save you $16.07 over the new one.
  • Used previous 1st edition costs $14.34 less than the used 2nd.



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