- Series: Chapman & Hall/CRC Texts in Statistical Science (Book 122)
- Hardcover: 487 pages
- Publisher: Chapman and Hall/CRC (December 21, 2015)
- Language: English
- ISBN-10: 1482253445
- ISBN-13: 978-1482253443
- Product Dimensions: 1 x 7.5 x 10.5 inches
- Shipping Weight: 2.6 pounds (View shipping rates and policies)
- Average Customer Review: 4.5 out of 5 stars See all reviews (21 customer reviews)
- Amazon Best Sellers Rank: #64,630 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
Use the Amazon App to scan ISBNs and compare prices.
Featured Springer resources in biomedicine
Explore these featured titles in biomedicine. Learn more
Frequently bought together
Customers who bought this item also bought
"… I am quite impressed by Statistical Rethinking … I like the highly personal style with clear attempts to make the concepts memorable for students by resorting to external concepts. … it introduces Bayesian thinking and critical modeling through specific problems and spelled out R codes, if not dedicated datasets. Statistical Rethinking manages this all-inclusive most nicely … an impressive book that I do not hesitate recommending for prospective data analysts and applied statisticians!"
―Christian Robert (Université Paris-Dauphine, PSL Research University, and University of Warwick) on his blog, April 2016
"Statistical Rethinking is a fun and inspiring look at the hows, whats, and whys of statistical modeling. This is a rare and valuable book that combines readable explanations, computer code, and active learning."
―Andrew Gelman, Columbia University
"This is an exceptional book. The author is very clear that this book has been written as a course . . . Strengths of the book include this clear conceptual exposition of statistical thinking as well as the focus on applying the material to real phenomena."
―Paul Hewson, Plymouth University, 2016
About the Author
Richard McElreath is the director of the Department of Human Behavior, Ecology, and Culture at the Max Planck Institute for Evolutionary Anthropology. He is also a professor in the Department of Anthropology at the University of California, Davis. His work lies at the intersection of evolutionary and cultural anthropology, specifically how the evolution of fancy social learning in humans accounts for the unusual nature of human adaptation and extraordinary scale and variety of human societies.
If you are a seller for this product, would you like to suggest updates through seller support?
Top Customer Reviews
I've personally spent a good deal of time researching and reading and I can unequivocally say this is the best book for anyone wanting to get started with Bayesian data analysis who has at least basic programming skills and a basic understanding of calculus. This book is not only a great learning resource, it's actually fun to read. The only downside to this book is that it is very expensive. It's definitely worth it if you're serious about learning Bayesian data analysis but I wish the book didn't have to be so expensive. I will say that the paper and binding seems high-quality though.
All in all I would say this is probably the most satisfied I've been with a book purchase in recent history. I cannot recommend this highly enough. I wish all math/programing/statistics books were this good. The manner and quality of teaching really makes all the difference.
It is very accessible -there is barely any math- and focuses on how to connect the principle behind each theory to its potential application, emphasizing scope, limitations and philosophy. I helps you to think statistically. On top of that, it shows you the applications with programming and coding.
Although I had already a decent training in stats, up to a first year of grad school, I found this book enlightening, full of insights, fun to read. There were many issues I already knew, but that I had not connected together. Definitely the best book I know -and I believe I know insanely many books on the topic- you can choose for an intermediate class on data analysis.
1. It starts with a stepped-through example explaining how to link priors, calculate likelihood and arrive at a posterior, all using a grid/matrix approach. Whilst not something one would normally do in real analysis, the basic concept is wonderfully illustrated here. It provides rock solid foundations for the later material.
2. Throughout the book there is R-code, along with a functioning download. The reader can try, fiddle, break and repair examples as much as need be to understand the concepts.
3. There is an excellent series of Youtube recordings to assist in understanding (highly recommended).
4. The end result is being able to use Stan, only utilising the rethinking package to implement models. The alternative to this approach is either to learn Stan (which is not hard, but subsequent to this book) or to use rstanarm (in CRAN). Both these options are more likely effective alternatives AFTER learning to use the rethinking package.
5. The focus on plotting and the useful wrappers to do so in the rethinking package quickly get the reader to a point of being able to use real data of relevance to him or her.
6. The wrap-up for the book is around GLM models and how these might productively be applied in research settings.
7. The focus on social sciences also is highly relevant given the controversies around some statistical procedures. Optimistically, this text offers a solid way forward for a graduate student into a research dissertation using Bayesian methods.
8. The rethinking package includes datasets to use in developing one's skills from this text.
What are the limitations? Possibly, it might have been useful to have greater clarity around how the wrappers work (e.g., map, map2stan). But even there, one can inspect the code, if required. Also, whilst I personally find the rethinking package to be quite brilliantly useful, compared to rstanarm, it is not clear whether it will remain current. This is more a gripe about R, which unlike Python, seems to believe that there a many many ways to construct good code, rather than one, best way.
I unreservedly recommend this text as a start and intermediate development point for an applied user of HMC Bayesian methods using Stan.