I tried to follow along with the textbook before but really struggled with the practical side - R is just another world in terms of dependency management and organisation/documentation (compared to python at least). The book had me install some version of a library that was since unsupported. So I thought I would be a nerd and do everything in python instead, but there I had other problems installing pymc. After some hours of failing I just gave up. Can anyone speak to the state of the dependencies in this edition? Has everything been updated? Versions listed? Would love to give this another shot

Related. Others?

Statistical Rethinking (2022 Edition) - https://news.ycombinator.com/item?id=29956390 - Jan 2022 (124 comments)

Statistical Rethinking [video] - https://news.ycombinator.com/item?id=29780550 - Jan 2022 (10 comments)

Statistical Rethinking: A Bayesian Course Using R and Stan - https://news.ycombinator.com/item?id=20102950 - June 2019 (14 comments)

It's a great book, but my personal opinion is that it would have benefited from an editor that recommended some small changes. The previous edition had a TOC which was barely usable because all funny jokes in chapter names like "8 Conditional Manatees". Besides, there were too many jokes embedded in some sections, which made them difficult to follow. I think some of these issues are getting addressed in the current edition.

Nonetheless, the book is very well written and all figures and examples show great attention to detail. I found Gelman et al Regression and Other Stories better for teaching newcomers, and surprisingly insightful. Statistical Rethinking is a good choice for a second course, but perhaps too informal at that stage.

I'm excited to see the online lecture videos. I previously bought the book from a recommendation I saw online, and have been working through the chapters and doing the exercises in self-study. But I stalled a bit as some of the later chapters were harder to follow. I hope that the video lectures will help.

I've taken the course and love the book. The key takeaways for me were to shun cleverness in favour of building demonstrably sensible models out of simple parts and clear causal assumptions. There's a lot of using models as random generators as a way to validate assumptions that demonstrates that your model makes sense before you start with inferencing which is a fantastic habit to stick with.