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
There is a version of everything in the book reimplemented in rStan, which is a fairly easy to install and well supported R package that wraps Stan. I don’t have the link but should be easy to google.
I think it’s a magnificent book - definitely repays the time to work though in detail.
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.
The videos are amazing. I watched the first season, and gave a glance at the second or third iteration, which somehow seem even better. He added one clutch visualization which really made a concept click for me.
I thought the book was only so-so, but required to support the nuances of what he discussed in class.
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
There is a version of everything in the book reimplemented in rStan, which is a fairly easy to install and well supported R package that wraps Stan. I don’t have the link but should be easy to google.
I think it’s a magnificent book - definitely repays the time to work though in detail.
For those looking for pymc and the python implementation of this book, here are the jupyter notebooks for the 2022 edition https://github.com/pymc-devs/pymc-resources/tree/main/Rethin...
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 second that. The TOC is unusable. However, it's probably aligned with the author's intention of it being a course and not a reference book.
The lectures are on YouTube and are really very good.
What are the prerequisites for the topics covered in this book? I feel like the lecture list is hard to understand, maybe sort of like the book’s TOC.
Honestly, I think there are very little prerequisites. I'm an MD dabbling into stats and found the book very well made as well as understandable.
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.
The videos are amazing. I watched the first season, and gave a glance at the second or third iteration, which somehow seem even better. He added one clutch visualization which really made a concept click for me.
I thought the book was only so-so, but required to support the nuances of what he discussed in class.