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Doing Bayesian Data Analysis: A Tutorial with R and BUGS [Hardcover]

John K. Kruschke
4.8 out of 5 stars  See all reviews (55 customer reviews)

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Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan 4.7 out of 5 stars (42)
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Book Description

November 10, 2010 0123814855 978-0123814852 1

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.

-Accessible, including the basics of essential concepts of probability and random sampling

-Examples with R programming language and BUGS software

-Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis).

-Coverage of experiment planning

-R and BUGS computer programming code on website

-Exercises have explicit purposes and guidelines for accomplishment


Frequently Bought Together

Doing Bayesian Data Analysis: A Tutorial with R and BUGS + Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) + An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Editorial Reviews

Review

"This book is head-and-shoulders better than the others I've seen.  I'm using it myself right now.  Here's what's good about it: •It builds from very simple foundations. •Math is minimized.  No proofs. •From start to finish, everything is demonstrated through R programs. •It helps you learn Empirical Bayesian methods from every angle…"--Exploring Possibility Space blog, March 12, 2014

From the Back Cover

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis).

This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.


Product Details

  • Hardcover: 672 pages
  • Publisher: Academic Press; 1 edition (November 10, 2010)
  • Language: English
  • ISBN-10: 0123814855
  • ISBN-13: 978-0123814852
  • Product Dimensions: 7.6 x 1.3 x 9.3 inches
  • Shipping Weight: 2.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (55 customer reviews)
  • Amazon Best Sellers Rank: #183,324 in Books (See Top 100 in Books)

Customer Reviews

4.8 out of 5 stars
(55)
4.8 out of 5 stars
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Top Customer Reviews
117 of 119 people found the following review helpful
5.0 out of 5 stars Best of the rest May 12, 2011
Format:Hardcover
I have reviewed a number of statistics texts for academic journals over the years, and have authored published reviews of some six books specifically devoted to Bayesian analysis. I consider John Kruschke's "Doing Bayesian Data Analysis" to be the best text available for learning this branch of statistics.

Learning how to craft meaningful statistical tests and models based on Bayesian methods is not an easy task. Nor is it an easy task to write a comprehensive basic text on the subject -- one that actually guides the reader through the various Bayesian concepts and mathematical operations so that they have a solid working ability to develop their own Bayesian-based analyses.

There are now quite a few texts to choose from in this area, and some are quite good. But Kruschke's text, in my opinion, is the most useful one available. It is very well written, the concepts unique to the Bayesian approach are clearly presented, and there is an excellent instructors manual for professors who have adopted the book for their classes. Kruschke uses R and WinBUGS for showing examples of the methods he describes, and provides all of the code so that the reader can adapt the methods for their own projects.

"Doing Bayesian Data Analysis" is not just an excellent text for the classroom, but also -- and I think foremost -- it is just the text one would want to work through in order to learn how to employ Bayesian methods for oneself.

Joseph Hilbe
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65 of 67 people found the following review helpful
Format:Hardcover|Verified Purchase
I highly recommend this book to two audiences: (a) instructors looking to construct a strong course on "introduction to social science statistics" from a Bayesian perspective; and (b) social science researchers who have been educated in a classical framework and wish to learn the foundational knowledge of a Bayesian approach, without a refresher in differential calculus. (I expect it would also of interest to many physical science and engineering researchers whose methods are not highly divergent from social science (e.g., biologists, operations engineers) but I can't speak authoritatively about that.)

I'm a practicing social science researcher and have wanted for years to learn Bayesian methods deeply - I've used them in applied settings but without complete understanding. My quest to learn Bayesian methods more rigorously has been persistently stymied by texts that demand analytic solutions to prior/posterior estimation, that are excruciatingly focused on specific problems with little attention to generalization, or that skip huge areas of exposition to leap from a toy problem to a complex one with little clue of the path between them. Dr. Kruschke's text avoids all of those problems. It is remarkable for building intuition from basic principles, for avoiding page-after-page of integrals, and for having extremely clear application.

The book starts by laying out the core intuitions of Bayes's rule - instead of merely stating it (and don't we all think we know it by now?), it leads the reader through some applied examples with frequency tables. Simple? Yes; but also valuable to force oneself through.
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45 of 46 people found the following review helpful
5.0 out of 5 stars Important book December 7, 2011
Format:Hardcover
As far as I am concerned, if you write a book this good, you get to put whatever you like on the cover - puppies, Angelina Jolie, even members of the metal band "Das Kruschke". While reading "DBDA" - reading *and* stepping through the code examples - will not make you a "Bayesian black-belt", it's impressive how much information it *will* give you - the book is almost 700 pages, after all - and you don't need (but it helps) to have tried to get the hang of the "Bayesian stuff" with other books to appreciate how friendly and effective this one is. (The author's explanation of the Metropolis algorithm is a good example). At the risk of sounding grandiose, the book just might do for Bayesian methods what Apple's original Mac did for the personal computer; here's hoping.

PS. Three worthwhile related (more technical) books:

"Data analysis using regression and multilevel/hierarchical models" by Gelman and Hill. (A very nice book, like "DBDA", but intentionally not-especially-Bayesian).

"Bayesian statistical modeling" by Congdon. (A survey of Bayesian applications).

"Dynamic linear models with R" by Petris et al. Prado and West. (A nice introduction to Bayesian approach to time series).

UPDATE. There is a new kid on the block - "Bayesian modeling using WinBUGS" by Ntzoufras. Although I am still a fan of "DBDA", I think that Ntzoufras's book would be a better bet for many people. Starting with "DBDA", and moving on to that book, may be best.
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27 of 29 people found the following review helpful
4.0 out of 5 stars Amazing October 3, 2011
Format:Hardcover
All of a sudden it just makes sense! Everyone knows that "lightbulb moment", when previously accumulated knowledge or facts become condensed into a lucid concept, where something previously opaque becomes crystal clear. This book is laden with such moments. This is the most accessible statistics text for a generation and I predict (based on prior knowledge) that it will be a major factor in moving scientists of every shape and size towards the Bayesian paradigm. Even if you're sceptical, you're likely to learn more about frequentist statistics by reading this book, than by reading any of the tomes offered by so called popularisers. If you are a social scientist, laboratory scientist, clinical researcher or triallist, this book represents the single best investment of your time. Bayesian statistics offer a single, unified and coherent approach to data analysis. If you're intimidated by the use of a scripting language like "R" or "BUGS", then don't be. The book repays your close attention and has very clear instructions on code, which elucidate the concepts and the actual mechanics of the analysis like nothing I've seen before. All in all, a great investment. The only serious question that can be raised about the design and implementation of a book such as this is: why wasn't it done before?
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Most Recent Customer Reviews
5.0 out of 5 stars I always avoid talking about MH or Gibbs sampling with biologists as I...
I have been working with biological researchers who understand stats in a shallow level, especially in Bayesian world. Read more
Published 2 months ago by Amazon Customer
5.0 out of 5 stars with nicely annotated blocks of code to use as a starting ...
It is a very readable math book,with nicely annotated blocks of code to use as a starting point for the exercises in the book, as well as Bayesian analysis in the real world.
Published 17 months ago by Amanda Rudelt
5.0 out of 5 stars This is a great book. If you use R and have interest ...
This is a great book.
If you use R and have interest in Bayes, you should buy this book.
The style is more pragmatic, less academic than Wiley texts. Read more
Published 17 months ago by W. Hogan
3.0 out of 5 stars Three stars because this good book, and the author fixed this in the...
Three stars because this a good book. With somewhat unclear definition of how Bayesian analysis differs from routine statistics. But the author fixed this issue in the new edition. Read more
Published 20 months ago by Jim Burke
5.0 out of 5 stars Five Stars
Excellent starter book for Bayesian analysis. I plan to buy the 2nd edition.
Published 20 months ago by Mary E. Graybeal
5.0 out of 5 stars Five Stars
excellent coverage of Bayesian data analysis
Published 22 months ago by Amazon Customer
5.0 out of 5 stars Fantastic introduction to Bayesian Statistics
This book is extremely well written for the autodidact. His writing style is extremely clear, witty, and amusing. Read more
Published 23 months ago by The Professor
5.0 out of 5 stars Worth it's weight in gold!!!
For practical application and meaningful comparisons to NHST methods, this book is second to none!!!
Published 24 months ago by James Linton
3.0 out of 5 stars Puppies!
Gave me ideas for how Bayesian statistics works. Good introductory text. Practical. It's very light on the mathematical justifications, which is fine since it's intended for the... Read more
Published on June 26, 2014 by Milo T Page
5.0 out of 5 stars a book with dogs on the cover!
I work at a college where a professor told me they would like to order this book. I did. They have not complained! Read more
Published on June 11, 2014 by Mister George, III
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