Statistical Rethinking: A Bayesian Course with Examples in R and Stan, 2/e (Hardcover)

The very popular Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition　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 main changes in the second edition are:

• Map2stan has been replaced by ulam. The new ulam is also much more flexible, mainly because it does not make any assumptions about GLM structure and allows explicit variable types within the formula list.
• Most modeling examples have some prior predictive simulation. This is the most useful addition to the second edition as it helps understanding not only priors but the model itself.
• Chapter 5 on multiple regression has been split into two chapters. The first chapter focuses on helpful aspects of regression. The second focuses on ways that it can mislead.
• Chapter 4 now ends with B-splines. The chapter on count models, Chapter 11, now includes an item-response (factor analytic) example. Chapter 12 contains a survival analysis with censoring. Chapter 14 has an example of a phylogenetic distance regression. The new Chapter 16 focuses on models that are not easily conceived of as GLMMs.
• There are new data examples such as the Japanese cherry blossoms historical time series and a larger primate evolution data set with 300 species and a matching phylogeny.
• There are several places where raw Stan model code is explained inside optional boxes. This makes the transition to working directly in Stan easier but the main text remains R script using the rethinking package's teaching tools.