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Statistical Rethinking: A Bayesian Course with Examples in R and Stan
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Statistical Rethinking: A Bayesian Course with Examples in R and Stan

4.69  ·  Rating details ·  147 ratings  ·  11 reviews
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers' knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understan ...more
Hardcover, 469 pages
Published December 21st 2015 by CRC Press
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 ·  147 ratings  ·  11 reviews

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Nov 04, 2017 rated it really liked it  ·  review of another edition
This is a great book, one that will influence me for the rest of my life as a data scientist. The style is informal, adventurous and open. It very much treats statistics as an open discipline where many approaches can "make sense", and it's all just a big playground really. This style, which is somehow common among Bayesian statisticians, speaks to me, much more than the rigid, one-correct-test-thinking that you can find in some books on statistics. Statistical Rethinking really inspired me, and ...more
Jan 27, 2019 rated it it was amazing
Shelves: statistics
This book is an exemplary introduction to the Bayesian thought process. It's additionally quite good for practicing and learning R. When reading this, you will likely learn and have fun; it's rare to find both of these (or, quite often, just one) in one text. The tone is very conversational and friendly, and Dr. McElreath doesn't take himself too seriously. If you choose to use this book, I would strongly recommend his excellent lectures on YouTube that accompany the book. Overall, I would stron ...more
Mar 05, 2018 rated it it was amazing
This book is a one-stop shop for learning statistical modeling.

The first six chapters demonstrate many of the concepts in Bayesian statistics and linear models, using fully-worked examples in R. Note that the R code leans heavily on STAN (through the rstan package) and the author's own rethinking package. This makes the examples small enough to be workable, and the mechanisms employed in the rethinking package are fully explained.

Chapters 7 through 12 gradually introduce new and more powerful mo
Benjamin Schneider
Sep 23, 2017 rated it it was amazing
The excellent writing in this book was a breath of fresh air compared to much of the genre of statistics textbooks. The author does a fantastic job explaining quantitative reasoning in clear English and developed a great set of metaphors and examples that are skillfully deployed throughout the book (e.g. models as golems; fitting of separate models rather than partially-pooled models as voluntary amnesia, etc.).

While I think the book's most valuable contributions are its clear explanations of s
May 28, 2019 rated it liked it
I liked it. A nice and short (~450 pages) introduction to the Bayesian modeling. I am a bit disappointed that the author created a high-level package that uses an R package (rstan) that then calls the package of interest ("STAN") to show some of the modeling principles. In a sense, this book is about modeling your approach to modeling and forming an intuitive and high-level of understanding without many implementation complexities and details. However, it leaves you with a hunger for more and wo ...more
Guillermo Duran
May 03, 2019 rated it it was amazing
A true jewel in terms of content and writing style. Using Jorge Luis Borges "The Garden of Forking Paths" story as an allegory of the likelihood function is the most elegant way I've seen to begin a stats book.
It's possible to feel the passion and knowledge of Dr. McElreath in every sentence of the book. This is one of those books that I will take with me to my lonely island and read over and over again.
Oct 04, 2017 rated it it was amazing
Shelves: datascience
The teaching approach used in the book is excellent. Specifically there is a very clear explanation of key statistics concepts and he uses simulation and graphics to reinforce these. In my opinion, this is one of the best intro's to statistics (pairs nicely with Bayesian Data Analysis). I added it to our company data science training curriculum. The accompanying videos are also worth looking out (and quite amusing).
Xiaoyu Lu
Jan 28, 2019 rated it it was amazing
Excellent narration with clear and interesting examples. Code snippets are provided in R. Some readers recreated the codes in this book using python and published it on Github (

Will definitely read again, and many times more.
Jan 21, 2016 rated it really liked it
Shelves: statistics-etc
If you read through this text you will get a great course in Bayesian statistics with lots of R code, many interesting asides, comparisons to frequentist methods and philosophical comments. I think I understand the Bayesian approach much better than I had before. In my limited experience, using this approach is still a lot of work, gives a near identical answer (since I've avoided p values for years anyway), and the principle advantage is that when the researcher/client tells you what they think ...more
Sean Martin
Jan 07, 2016 rated it it was amazing
Builds statistical methods from the ground up, from a pleasantly pragmatic viewpoint while providing a solid introduction to a set of very useful tools for real applications. Also gives much more in-depth insight into motivation than many intro texts.
May 09, 2016 rated it it was amazing
Best statistics (philosophy) book I have ever read!
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