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Statistical Rethinking: A Bayesian Course With Examples In R And Stan by Richard Mcelreath, T&F/Crc Press, 2016, Hardcover, 9781482253443
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"... 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
"The book contains a good selection of extension activities, which are labelled according to difficulty. There are occasional paragraphs labelled ‘rethinking’ or ‘overthinking’ that contain finer details. The presentation is replete with metaphors ranging from the ‘statistical Golems’ in Chapter 1 through ‘Monsters and Mixtures’ in Chapter 11 and ‘Adventures in Covariance’ in Chapter 13."
―Diego Andrés Pérez Ruiz, University of Manchester
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.
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.
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