Courses that use Doing Bayesian Data Analysis?

Instructors seek examples of using the book, Doing Bayesian Data Analysis, as part of a course. A cursory web search yielded these few listed below, but there must be others. For example, my own course web page did not show up in the search, nor did another course from which a published review of the book arose. Please let me know of other courses that use the book, and I will update this list in a subsequent post.

• Applied Bayesian Modeling for the Social Sciences, Dave Armstrong
• Bayesian Methods, Nick Beauchamp  
• Statistical & Cognitive Modeling for Formal Semantics, Adrian Brasoveanu  
• Introduction to Bayesian Analysis, Brad Carlin  
• Doing Bayesian Data Analysis, John Kruschke 
• Applied Bayesian Statistics, Marco Steenbergen 
• Statistics VI, Tuerlinckx and Vanpaemel (from which this book review arose)
• Bayesian Data Analysis, Uppsala University 
• Topics in Quantitative Psychology: Bayesian Data Analysis (no web page), Caren Rotello, U. of Massachusetts

Classroom-based review in J. of Math. Psych.

In a recent review (full citation below) in the Journal of Mathematical Psychology, Wolf Vanpaemel and Francis Tuerlinckx report results from using the book in a classroom setting. They say

Overall, Kruschke is to be applauded for his incredible efforts at writing such a highly accessible and useful textbook on Bayesian statistics. Doing Bayesian Data Analysis is an impressive piece of work that presents a major step in the dissemination of the Bayesian approach into mainstream psychology and will shape the way future psychologists will deal with their data. We are delighted to use it again in our course and wholeheartedly recommend it to anyone who wants to acquaint students with Bayesian statistics...

Big thanks to Wolf and Francis for using the book, and for the thoughtful review!

Vanpaemel, W. & Tuerlinckx, F. (2012). Doing Bayesian data analysis in the classroom: An experience based review of John K. Kruschke’s “Doing Bayesian Data Analysis: A Tutorial with R and BUGS” 2011. Journal of Mathematical Psychology, 56, 64–66.  DOI: 10.1016/j.jmp.2011.12.001

Some more nice reviews on Amazon.com

It's greatly appreciated when people go to the effort to write a nice review on Amazon. It's appreciated not only by the author :-) but crucially also by prospective readers who are trying to decide whether the book is worth getting. Here are some excerpts from some recent reviews on Amazon.com

This is one of the best written and accessible statistics books I've come across. Obviously, a lot of thinking went into coming up with examples and intuitive explanation of various ideas. I was consistently amazed at author's ability to not just say how something is done but why it is done that way using simple examples. I've read far more mathematically sophisticated explanations of statiscal modeling but, in this book,I felt I was allowed to peek into the mind of previous authors as to what they were really thinking when writing down their math formulas. (Posted November 11, 2011 by Davar314, San Francisco, CA)

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. (Posted December 7, 2011 by Dimitri Shvorob)

Click here for the full text of all the reviews on Amazon. Thanks again, reviewers, for the nice comments and for helping prospective readers.

Happy Birthday, Puppies!

Happy Birthday, Puppies! Today the book turns one year old. Woohoo!

(But they have yet to turn a first penny in royalties. Fortunately, the real cake is more people doing Bayesian data analysis. That's a reason to celebrate!)

Review in PsycCritiques

In a a recent review^* in the online APA journal PsycCRITIQUES, reviewer Cody Ding says

There are quite a few books on Bayesian statistics, but what makes this book stand out is the author’s focus of the book—writing for real people with real data. Clearly a master teacher, the author, John Kruschke, uses plain language to explain complex ideas and concepts. This 23-chapter book is comprehensive, covering all aspects of basic Bayesian statistics, including regression and analysis of variance, topics that are typically covered in the first course of statistics for upper level undergraduate or first-year graduate students. A comprehensive website is associated with the book and provides program codes, examples, data, and solutions to the exercises. If the book is used to teach a statistics course, this set of materials will be necessary and helpful for students as they go through the materials in the book step by step.

My thanks to Cody Ding for taking the time and effort to write a review (and for such a nice review too!).
^* Ding, C. (2011). Incorporating our own knowledge in data analysis: Is this the right time? (Book Review) PsycCRITIQUES, 56(36). DOI: 10.1037/a0024579

Another reader's rave review

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?

Click here to see the review at Amazon.com. My great appreciation goes to R. Dunne for taking the effort to post the review.

Review in the British Journal of Mathematical and Statistical Psychology

In a recent review of the book in the British Journal of Mathematical and Statistical Psychology*, Mark Andrews says

Though risking hyperbole, I would describe this book as revolutionary, at least in the context of psychology. It is, to my knowledge, the first book of its kind in this field to provide a general introduction to exclusively Bayesian statistical methods. In addition, it does so almost entirely by way of Monte Carlo simulation methods. While reasonable minds may disagree, it is arguable that both the general Bayesian framework advocated here, and the heavy use of Monte Carlo simulations, are destined to be the future of all data analysis, whether in psychology or elsewhere. If this is so, then Doing Bayesian Data Analysis might be something of a harbinger, rousing psychology to the new realitites of data-analysis in the 21st century. ...
Doing Bayesian Data Analysis introduces psychology to new ways of thinking and new ways of talking about and presenting data-analysis. Anyone serious about data analysis in psychology ought to read this book. At the very least, it will serve as a welcome new perspective on the field. More probably, or so it seems to me, the ideas and methods presented here will eventually be seen as the foundations for new approaches to statistics that will becomes commonplace in the near future.

I think the reviewer has a remarkably clear view of the intellectual landscape, of where psychology (and science generally) is going, and of where the book attempts to situate itself. Thank you, Mark, for such a perceptive review. Now may everyone else see as clearly as you!

* Andrews, M. (2011). Book Review. British Journal of Mathematical and Statistical Psychology. Article first published online: 5 Sep 2011. DOI: 10.1111/j.2044-8317.2011.02027.x

Another review from a reader

Here's another review, with an extensive summary, apparently from a reader in India:
http://rkbookreviews.wordpress.com/2011/08/27/doing-bayesian-data-analysis-summary/
The reviewer's nom-de-blog is "safeisrisky". So, whoever you are, thank you for the nice review!

Review from Dr. Joseph Hilbe

Posted on Amazon.com, May 12, 2011, by Dr. Joseph Hilbe:

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.

Thank you, Joe!

Click here to see Joe's books on Amazon.com.

Review in Journal of Mathematical Psychology

After a few hundred words of criticism in his recent review in the Journal of Mathematical Psychology*, Michael Smithson concludes:

"All said and done, the criticisms I have raised here are relatively minor. This is the best introductory textbook on Bayesian MCMC techniques I have read, and the most suitable for psychology students. It fills a gap I described in my recent review of six other introductory Bayesian method texts (Smithson, 2010). I look forward to using it in my own teaching, and I recommend it to anyone wishing to introduce graduate or advanced undergraduate students to the emerging Bayesian revolution."

Thank you, Michael!

* Smithson, M. (in press). Book Review. Journal of Mathematical Psychology. doi:10.1016/j.jmp.2011.05.002

Smithson, M. (2010). A review of six introductory texts on Bayesian methods.
Journal of Educational and Behavioral Statistics, 35, 371–374.

P.S. Michael comments about teaching non-Bayesian data analysis on his blog.

Maligned Puppies! (Review in Journal of Economic Psychology)

In a recent review of the book in the Journal of Economic Psychology*, Dan Goldstein perspicaciously says 

"A person would have to make an effort not to learn this material after following this tutorial. The book is relentlessly clear. Topics are explained analytically as well as visually and code is provided with which the reader can see and change every assumption made." 

Despite this brilliant and insightful assessment, Dan later states "The cover has puppies on it. Yes, puppies. Had paper grocery bags not disappeared from supermarkets, I would have covered my copy to avoid the strange looks my thoroughly quantitative colleagues gave me as I spent weeks working through the book."  

Well, the solution to this problem is just a Post-It away! See photo at right.

Thank you, Dan, for working through the book and writing such a thoughtful review.

P.S. As explained at this other blog entry, the happy puppies are named Prior, Likelihood, and Posterior. The Posterior puppy has half-up ears, a compromise between the perky ears of the Likelihood puppy and the floppy ears of the Prior puppy. (The puppy on the back cover is named Evidence. MCMC methods make it unnecessary to explicitly compute the evidence, so that puppy gets sleepy with nothing much to do.)

* Goldstein, D. G. (2011). Book review. Doing Bayesian Data Analysis: A Tutorial with R and BUGS, John K. Kruschke. Academic Press, Elsevier (2011). ISBN-13: 9780123814852. Journal of Economic Psychology, 32(5), 724-725. doi:10.1016/j.joep.2011.05.010