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Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)

4.5 out of 5 stars 19 customer reviews
ISBN-13: 978-1482253443
ISBN-10: 1482253445
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  • Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
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Product Details

  • 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: 7 x 1 x 10.1 inches
  • Shipping Weight: 2.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (19 customer reviews)
  • Amazon Best Sellers Rank: #61,194 in Books (See Top 100 in Books)

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Top Customer Reviews

Format: Hardcover
This is the perfect book for people that have some knowledge of statistics (maybe one course) and want to get into Bayesian statistics. It's a pedagogical masterpiece that includes just the right amount of math. It's also extremely pragmatic, both telling how to do thing, while at the same time not skimping on the why. I just can't recommend this book enough!
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Format: Hardcover
This is a very readable and even *entertaining* text that serves as an integrated treatment of the history, philosophy, and practical implementation details (in R) of state of the art quantitative methods. The introduction to Bayesian thought that is provided is the most accessible that I have encountered, and for this alone I am sure I will continually reference this text and I will certainly share it with others. This text also stands out because of the data sets, example problems, and analytical forks in the road that McElreath leads us to. These examples will feel very familiar and relevant to practicing scientists in academia (e.g. public health, social sciences, biological sciences) or data analysts in the private sector. It provides original and accessible introductions into the assumptions and practical details of multivariate regression, generalized linear models, MCMC estimation, and hierarchical modeling. It also provides an excellent discussion of the perils of over-fitting one's models, and the role of information criteria in carrying out model comparison. If you have taken an "intro to statistics" class but now want to learn the craft as practiced by professionals, this is the book to get. I could have saved myself hundreds of hours of self-directed trial and error learning in graduate school (and after) had this text been available. The content of this book has been developed over a decade+ of McElreath's teaching and mentoring of graduate students, post docs, and other colleagues, and it really shows. This book's huge achievement is its ability to provide great coverage of relevant history, theory, and nitty-gritty practical implementation details in a coherent and readable fashion.
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Format: Hardcover
Probably the best book on Bayesian inference I've read. Dr. McElreath has a very clear, informative yet entertaining way of explaining these concepts and the book includes excellent example code in R (available at github as well). Also, lectures from one of his courses that follow this book are available on youtube.
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Format: Hardcover Verified Purchase
I don't usually comment, but I just wanted to say the book is outstanding.

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.
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Format: Hardcover
Trying to make the jump from frequentist to Bayes can be a struggle. I found other books to be either highly mathematical or focused on only the most basic of models. This book was, for me, somewhat of a breakthrough. It's very well written, exceptionally clear, and gets to many of the advanced topics I'm interested in. The code is also beautiful, and helped me to learn more about R programming, generally. I really can't recommend this book any higher to anyone trying to learn Bayes. Also (others may have mentioned this), his lectures are all available on youtube [...] and they are also exceptional. If you buy and read the book, and watch the lectures, you'll be in great shape, and really getting a bargain of a deal.
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Format: Hardcover Verified Purchase
This book is unbelievably great. I don't have a great math background but I do have a significant programming background, so understanding algorithms in terms of code is always much easier than trying to decipher the math; so since this book mostly focuses on code, it makes it that much easier. But more importantly than that, this book really attempts (and succeeds) to give an intuitive understanding of all the concepts rather than delivering a protocol for performing Bayesian analysis. I've read most of Kruschke's "Doing Bayesian Analysis" and while that book is perhaps more comprehensive in what it covers, and arguably has better graphics, this book blows it out of the water (and it's like half the length). Reading other Bayesian statistics books and documents made me think "I kind of get it" but after reading this book everything just clicked. I not only understand the basic procedures of Bayesian analysis but the underlying reasons as to where all of this came from and why we do things the way we do. The author uses clear down-to-earth examples to illustrate all major concepts and avoids or clearly explains any technical jargon making this perhaps THE most accessible book on Bayesian analysis on the market. He also knows where to dive into details and where abstracting a bit is most appropriate. There's also little in situ boxes ("Overthinking") with optional information if one wants to know more details about the current topic. Moreover, the "rethinking" R package that is used in the book is great. It comes with very useful helper functions to focus on learning concepts rather than wasting time explaining code minutiae, and it also has built in data sets for practice that are great.Read more ›
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