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.

Chapter 1 The Golem of Prague chapter 2 Small Worlds and Large Worlds chapter 3 Sampling the Imaginary chapter 4 Linear Models chapter 5 Multivariate Linear Models chapter 6 Overfitting, Regularization, and Information Criteria chapter 7 Interactions chapter 8 Markov Chain Monte Carlo chapter 9 Big Entropy and the Generalized Linear Model chapter 10 Counting and Classification chapter 11 Monsters and Mixtures chapter 12 Multilevel Models chapter 13 Adventures in Covariance chapter 14 Missing Data and Other Opportunities chapter 15 Horoscopes