Integrates code fully into the main text, allowing readers to implement the code and compare results Illustrates many concepts through worked data analysis examples Presents full explanations of the code, enabling readers to diagnose and fix problems Explains important and unusual programming tricks Shows how the same mathematical statistical model can sometimes be implemented in different ways Offers more detailed explanations of the mathematics in optional sections Provides the R package on the author’s website and GitHub A solutions manual and figure slides are available upon qualifying course adoption. Summary 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. Share this Title Related Titles 1 of 1 Bayesian Data Analysis, Third Edition Recommended For You