A meta post introducing my solutions to the fantastic excellent second edition of “Statistical Rethinking” by Richard McElreath, a.k.a. Statistical Rethinking². Also discusses strategies to keep up with the material, mostly meant for self-study groups.


As detailed previously, I recently was part of a course centered around Bayesian modeling for the Icelandic COVID-19 pandemic. The Bayesian mindset needs no introduction, and this post is completely inadequete to explain why anyone should be interested (that’s what the book is for!). That said, especially for self-paced study groups, it might help to have some structure.


These are meant to be sample solutions, and everyone should solve these for themselves. Each solution contains the packages used, as well as a colophon in the later posts to ensure reproduciblity. Essentially this consists of four posts:

Week I
Covers the first four chapters {1,2,3,4}
Week II
Covers the next three chapters {5,6,7}
Week III
Covers five chapters {9,11,12}
Week IV
The last five chapters {13,14}

More concisely:

1. The Golem of PragueN/A
2. Small Worlds and Large Worldshere
3. Sampling the Imaginaryhere
4. Geocentric Modelshere
5. The Many Variables & The Spurious Waffleshere
6. The Haunted DAG & The Causal Terrorhere
7. Ulysses’ Compasshere
8. Conditional ManateesN/A
9. Markov Chain Monte Carlohere
10. Big Entropy and the Generalized Linear ModelN/A
11. God Spiked the Integershere
12. Monsters and Mixtureshere
13. Models With Memoryhere
14. Adventures in Covariancehere
15. Missing Data and Other OpportunitiesTBA
16. Generalized Linear MadnessTBA
17. HoroscopesN/A


The solutions compiled here were from an accelerated 4-week course covering the Statistical Rethinking² in four weeks. The book is more traditionally used in a full-semester course, so that should be kept in mind as well.


These are highly opinionated and the following list is in no way complete.

Canonical Content

Additional Content

Follow-up Courses


This has been a short meta post which is essentially meant to collect content posted with dates in the past. Though this is not exactly a complete reference for beginners, it might still help people.