"A CRC title." Includes bibliographical references and index.

The golem of Prague -- Small worlds and large worlds -- Sampling the imaginary -- Linear models -- Multivariate linear models -- Overfitting, regularization, and information criteria -- Interactions -- Markov chain Monte Carlo -- Big entropy and the generalized linear model -- Counting and classification -- Monsters and mixtures -- Multilevel models -- Adventures in covariance -- Missing data and other opportunities -- Horoscopes. "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." --Publisher's website.