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Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science), by Richard McElre
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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 understand enough of the details to make reasonable choices and interpretations in their own modeling work.
The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.
Web Resource
The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
- Sales Rank: #20830 in Books
- Published on: 2015-12-21
- Original language: English
- Number of items: 1
- Dimensions: 10.10" h x 1.00" w x 7.00" l, .0 pounds
- Binding: Hardcover
- 487 pages
Most helpful customer reviews
27 of 27 people found the following review helpful.
A pedagocal masterpiece. A great way to get into Bayesian statistics, or really just statistics in general.
By Rasmus B��th
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!
21 of 22 people found the following review helpful.
A very original and readable text that might transform you into a data analysis wizard!
By Brian M. Wood
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.
12 of 12 people found the following review helpful.
The Best Bayes Book
By John K
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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