Statistical Rethinking - A Bayesian Course with Exampls in R and Stan - Grand Format

"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. Statistical Rethinking : A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated.
This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your 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.
Features : Integrates code fully into the main text, allowing you to implement the code and compare results. Illustrates many concepts through worked data analysis examples. Presents full explanations of the code, enabling you 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.