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Showing 1-10 of 43 reviews(Verified Purchases). See all 58 reviews
on May 6, 2011
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. It then builds upon this knowledge systematically, going through the requisite coin toss examples - but unlike most texts, connecting them clearly to real-world examples of binomial problems. And it proceeds from there, ending up with Bayesian versions of ANOVA-type problems and logistic regression.

There are two other salient and important features of the book. First, the exercises are particularly well-chosen to reinforce the key points and demonstrate applications. I strongly recommend to work your way through them. In my case, for instance, they forced me to confront understanding of things like the "prior likelihood of the data" - a core concept that I thought I understood but really didn't until I had to solve some actual problems.

Second, the book is closely linked to the R statistics environment - surely the most popular tool used by Bayesian statisticians - and has sample programs that are illustrative, useful, and actually work. If you do Bayesian work, you're probably going to use R, and these examples will help immensely to build the set of tools you'll need.

Finally, and just to make clear, I have a disrecommendation for one audience: if you're looking for a highly mathematical treatment of Bayesian methods, it is not the right book. It is a didactic text, not a reference manual or set of derivations.

Good luck to you as a reader, and thank you to the author!
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on December 11, 2013
I use this book as a recommended text in both classes (an "advanced data analysis and applied regression" PhD seminar) and with my research assistants. This is definitely the most accessible text on Bayesian methods for psychologists. The numerous worked examples, code in R, BUGS, and JAGS, and the solution manual available from the author's website, all make this book excellent for self-guided study. You would have to be actively resisting it to not learn Bayesian data analysis! The writing is always completely clear, and the methods are built up from the simplest to very complex in intuitive steps. Assumes essentially no background beyond fairly basic algebra and just enough calculus to more-or-less remember what an integral does. I believe it could be used as a first statistics book, if the instructor didn't feel the need to inculcate traditional frequency-based methods first!
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on August 10, 2014
This book is extremely well written for the autodidact. His writing style is extremely clear, witty, and amusing. Some downsides are BUGS is a pain in the arse to work with, so a lot of the programming exercises become very difficult, and he doesn't really spend a whole lot of time introducing you to the workings of the R programming language properly. In retrospect, I would purchase a book like Learning R by Richard Cotton first, work through it, then tackle this book. But as far as the BUGS problems goes, the author has recommend you use JAGS instead, but that is not in the answer key unfortunately so if you are an autodidact that makes this difficult. In November 2014 a second edition that uses JAGS instead, as well as adding in STAN, is coming out, so if you are reading this prior to Nov. 2014 hold out for the second edition, or if you are reading this AFTER Nov. 2014, be aware of the second edition!
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on November 11, 2011
Theoretically: The author has decided to isolate and concentrate his attention on the most fundamental ideas of statistical modeling using Bayesian approach. He provides intuitive explanations of these ideas and not just formal mathematical derivations. This is an ambitious goal of providing the reader with deep conceptual and intuitive understanding of what statistical modeling is really all about.

Practically: Programming using R is used to show implementation of various algorithms.

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.

The R code examples seamlessly integrated into theory provide a practical road map to do actual analysis.

I highly recommend this book even if you are not particularly interested in Bayesian frame work. This book provides invaluable insights into what statistical modeling is all about.
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on May 11, 2013
The book has plenty of code which can be readily adapted to your own data analysis workflow. Some of the examples can be easily expanded to fields like life sciences, reliability quality engineering, and risk assessment. It provides one of the indispensable tool sets to bridge the data scientists and domain specialists. Having used WinBUGS, openBUGS/JAGS, python pymc package, and STAN, I would say the examples in this book using Brugs or rJAGS R library packages are much easier to implement. It can be an excellent starting point in bayesian data analysis. The only thing I wish I could find in this book is the application of multivariate normal distribution (inverse Wishart etc). Overall, I still think it is worthy of five star.
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on February 8, 2015
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.
The R code provided is clear, well written and demonstrative.

Further, the author provides a streamlined agenda, like, if you want a quick overview, here are the chapters to read: ...
This was considerate.
I found this book so interesting I wound up reading the interim materials as well.
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on April 22, 2013
If you want to learn Bayesian stats on your own, then this is a good book to learn it from. But, take your time. There's a wealth of information in the text, and if you move too fast (personal experience), then you will pay a penalty.

One thing I have never understood is WinBUGS. It's used in the book and I can't believe that this is the "best" software available. This is not a fault of the book though.

I liked the explanations overall, but the only shortcoming is sometimes the results of some of the calculations were limited.
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on August 10, 2012
I bought this book about a year ago, but I'm finally putting in the effort to go through it methodically this summer. Let me tell you, it's an extremely rewarding experience for me so far, and I'm only through 9 chapters at this point. The reviews were not lying. Kruschke explains Bayesian statistics in the same way a good teacher would if you were actually sitting there in the classroom. There is some theory in here, but mostly this book is for people like me who know they need to start incorporating Bayesian philosophy/methods in their work. What I love about this book is how far it takes it each example and explores it from start to finish. Virtually no stone is left unturned as the analyses become progressively more complex. I've read about 200 pages so far, and for every question that popped up in my head, the book would eventually provide me with the answer. This is really a very rare math book in my opinion. I'm an engineer by training, not a mathematician, so I don't know if this book is appropriate for "pure math" folks. But if I were designing an applied stats course for non-mathematicians (engineers, life scientists, etc.), this would be the textbook. I've never liked p-values and now I don't have to! Thanks, Dr. Kruscke. I'd say I can't wait for the second edition, but honestly, I'm not sure how much better it could get. Or put another way, the likelihood of the posterior improving on the prior is very low.
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on February 19, 2012
After reading lots of books and articles that left me wondering what bayesian data analysis was all about, this one really starts from the beginning, and teaches you everything you wanted to know about bayesian statistics. Not only that, but it points out important weaknesses in frequentist statistics they do not tell you about in publications. A must read for everyone interested in science...
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on March 1, 2015
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.
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