The core material ranges from the basics of regression to advanced multilevel models. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data.

* 1st edition translated to Python & PyMC3 * 1st edition examples translated to brms syntax: Statistical Rethinking with brms, ggplot2, and the tidyverse * Code examples from the book in plain text: code.txt * a new PyMC translation for the 3rd edition draft examples * Another Julia code translation with clean outline in notebook format * Julia & Turing examples (both 1st and 2nd edition) * R code examples from the book: code.txt * R package: rethinking (github repository) Book sample: Chapters 1 and 2 (2MB PDF).For more detail about what is new, look here. Publisher information on the CRC Press page. Most recent set of free lectures: Statistical Rethinking 2023 Second Edition A Bayesian Course with Examples in R and Stan (& PyMC3 & brms & Julia too, see links below)