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Doing Bayesian Data Analysis: A Tutorial Introduction with R
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Doing Bayesian Data Analysis: A Tutorial Introduction with R

4.37  ·  Rating details ·  322 ratings  ·  21 reviews
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 m ...more
Hardcover, 653 pages
Published October 27th 2010 by Academic Press
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Tobias Wolf
Sep 23, 2016 rated it it was amazing
A great introduction to a very important topic in statistics. When I started getting into Bayes unfortunately I did not choose this book because it looked a bit... weird with those puppies. But in fact this should really be no reason to stay away from it.
Marc Cooper
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 expre
Sep 21, 2014 rated it it was amazing
Chapter four and so far so good. Very clear. I'd tried a more technical book on this subject a few years ago (Gelman Carlin Stern and Rubin), and I found it tough going. It assumed you already knew about what it was talking about. This, book, on the other hand, is very discursive and has lots of illustrations, and I feel I'm understanding it. Probably at the end it'll be time to go back to Gelman at al. But meanwhile this is a great leg up!
Apr 25, 2018 rated it really liked it
Easily the best book detailing Bayesian statistics.

It’s easy to understand, explains everything well, and offers up so much about the topic. You do not need to be an expert in the field to understand this book, as this book takes you from the basics through to a more complex understanding.

Certainly worth reading if you want a better understanding.
Joe Cole
Aug 03, 2017 rated it it was amazing
If you need to learn how to perform Bayesian statistics and understand it, then this is the book for you. I have been looking for a book like this for 3 years. I get Bayes Rule, but when I tried plunging into Gelman's excellent book, it just assumed too much and I stumbled around. This book was slightly basic for me, but the examples and exercises have been just right for helping things to click for me.
Andy McKenzie
Mar 04, 2016 rated it it was amazing  ·  review of another edition
Shelves: statistics
This is the best statistics textbook I've read, and I've read at least parts of ~ 10 of them. I've also read many tutorials/explanatory articles online, and this competes with the best of them. The text is exceptionally clear and even somewhat addictive, which I was not expecting from a statistics book. I can think of a few reasons for this. First, Kruschke motivates why you should care. For example, one of the canonical examples that he returns to often is coin flipping. Instead of assuming tha ...more
Risto Hinno
Aug 25, 2016 rated it it was amazing  ·  review of another edition
I cannot look at the t-test the same way. If you haven't heard of anything Bayesian data analysis, this should be your first book to read. If you already know Bayesian data analysis you should still read the book. It is a nice intro to Bayesian data analysis with detailed explanation and with practical examples (it is very rare to get both in one book). I think statistics courses should teach this stuff. Teaching only usual stuff - t-tests, linear regression (as I studied in my time) is crime ag ...more
Dec 03, 2010 rated it it was amazing
Shelves: statistics
The best text I have yet read on Bayesian data analysis.
Aug 30, 2020 rated it it was amazing
Shelves: data-analysis
Great and gentle introduction to Bayesian Data Analysis. The examples and exercises are down-to-earth and doable, which really makes the learning experience great. After reading this book I also read "A Student's Guide to Bayesian Statistics" and "Statistical Rethinking" by Richard McElreath and I found myself reusing the knowledge and experience I acquired while reading the DBDA book. Highly recommended to newcomers to Bayesian Data Analysis.
Terran M
Mar 21, 2018 rated it it was amazing  ·  review of another edition
This is my favorite introductory book on Bayesian data analysis. It's extremely accessible, taking you through both the theory of how and why to use Bayesian techniques, and the practical matters of using JAGS to run models.
Great introductory text to applied Bayesian analysis
Eric Lawton
Jun 26, 2016 rated it it was amazing  ·  review of another edition
Shelves: maths
I don't think you could write a better book on this exciting topic.
I agree with its self-assessment on the skill level required of early-graduate student or final-year undergraduate. You need enough calculus to know what a differential or integral expression means but not how to evaluate it. The rest of the maths is what most call algebra but mathematicians call arithmetic, i.e. manipulating expressions with variables, not group theory or the like.
It is aimed at those who want to solve real-wor
Tom Schulte
Apr 30, 2016 rated it it was amazing  ·  review of another edition
Shelves: maa-reviews
Both textbook and practical guide, this work is an accessible approach to Bayesian data analysis from the basics. Chapter-length explorations of various implementations make this an effective reference for non-expert practitioners that seek to bring the value of Bayesian analysis to problems in their field. Intended for first-year graduate students or advanced undergraduates, this book offers thorough training on modern Bayesian methods for data analysis. Algebra and basic calculus, nothing real ...more
Michael Culbertson
Jan 26, 2016 rated it it was amazing  ·  review of another edition
Shelves: statistics
A fabulous (and thorough!) entry-level text on applied Bayesian data analysis (very likely the best intro text currently available). No background in statistics is strictly required, though students familiar with the basics (means, standard deviations, probability distributions, linear models, etc.) will have a bit of an easier time with the material. The presentation does make use of calculus, but the gist can be understood without it, and Kruschke is careful to walk through any derivations in ...more
Alexander Whyte
Oct 07, 2018 rated it really liked it
This is a very good book. One thing to watch out for is that the hierarchical models do not work with OpenBugs/BRugs. I have an old version of the book, so I'm not sure if newer versions already address this. They work with JAGS/rjags however.

It is very well written. The explanations are very clear.
Nov 13, 2016 rated it it was amazing  ·  review of another edition
Fantastic deep introduction to Bayesian techniques for data analysis. Really everything you need, and they teach you the tools you need as well. They use R as the data analysis language, which is very easy to use and you can easily translate the examples to Python or whatever your favorite language for data analysis might be.
Jan 28, 2017 rated it it was amazing  ·  review of another edition
Easily the best overview of MCMC modeling I've come across. Really never read a textbook that was so relatable while not dumbing down the material. One of the best statistics books I've read in years!
Oct 02, 2012 rated it it was amazing
Shelves: statistics
I'm coming from the point of view of someone who already does Bayesian statistics and wanted a textbook to recommend to others who'd also like to learn. This is the best one I've seen, by far.
Jun 02, 2013 rated it it was amazing
Shelves: statistics
Clear and utterly fascinating. The best thing I can say after reading this book is that Bayesian analysis indeed makes sense!
Sean Martin
Mar 03, 2015 rated it really liked it  ·  review of another edition
Well written and clear intro to some fairly difficult topics. Manages to bring up some of the more challenging technical aspects without overwhelming the reader with them.
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