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Doing Bayesian data analysis : a tutorial…
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Doing Bayesian data analysis : a tutorial with R, JAGS, and stan (edição 2015)

por John K. Kruschke

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Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.… (mais)
Membro:Radclyffe
Título:Doing Bayesian data analysis : a tutorial with R, JAGS, and stan
Autores:John K. Kruschke
Informação:London, UK ; San Diego, CA : Academic Press, [2015]
Coleções:Books at Home
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Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan por John Kruschke

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Sometimes a book can be too wordy; as here. In addition, there's a lot of repetition – intended, presumably, to aid reinforcement – which I found more confusing than helpful. There's an art to concision, and this tome could do with some serious editing.

The exercises are good for reinforcement, although I think they would have provided more benefit within the text during the early chapters instead of concentrated at the end. (Also, the associated code is rather messy; not that R is the most expressive of languages.)

Part I (chapters 1 thru 5) provides a great start to the topic up to introducing Bayes' Rule. Things start to begin to wobble with Part II chapter 6, which introduces the Benoulli distribution and the Beta function. I had to go to another text to make sense of the content and what it was trying to impart. Someone with some familiarity of these topics might have been less befuddled. Similarly, chapter 7 on Markov Chain Monte Carlo started out well, then dumped a lot of info over more that 40 pages before the exercises offered some relief and an opportunity to try and make sense of what had come before.

The text has an odd mixture of writing. Sometimes it's verbose like an introductory book, then it makes unexplained leaps into mathematics with little or no explanation that leave you scratching your head.

Subsequent chapters to above (the remaining 550 pages!) made more use of code within the text, which was a marked improvement.

Overall, not a bad book by any means. Just too many words! ( )
  ortgard | Sep 22, 2022 |
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Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

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