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Comment by _yb2s | Hacker News Reader

I want to second the recommendation for Statistical Rethinking from that review, it’s one of the best books ever written on computational Bayesian inference.

However, it is R based, and therefore arguably not an alternative to the book we are discussing here.

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As it happens, there's a PyMC implementation of the 1st and 2nd editions of Statistical Rethinking here:

https://github.com/pymc-devs/pymc-resources

(I think the author of the book discussed above, Osvaldo Martin, is the primary or sole contributor for the Rethinking implementations, in fact -- he had a full implementation in his own repo (https://github.com/aloctavodia/Statistical-Rethinking-with-P...) before deprecating it in favor of the above-linked one.)

You are cheating yourself if you only read the book. His lecture series covering the material on Youtube is phenomenal.

I went through the book + season 1 videos, and had a glance at some of the season two videos. The season two has some visuals that made some intuition click for me.

Now I see he has a 2023 playlist, which may yield even further improvements: https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XH...

I actually took his college course the YouTube videos are lecture recordings of

Agreed - that book is phenomenal at communicating the theory and mindset of Bayesian analysis. My favorite quote from the lectures - "Statistics should be subbordinate to Science." A very healthy emphasis on logic and proper model design - there will be no shoving data into black boxes!

R is a good language for this but he uses a library of convenience functions that are not on CRAN and are effectively just for educational purposes. So even if you want to stick with R, you'll need to translate your code into production-ready libraries anyway. There are several nearly-complete translations based on other R packages as well as ported to Julia and Python.

Or you could just put his code into production (source: I worked at a super successful gaming startup that had this code scattered all over their repo).

They had a bunch of other code problems too, but that was definitely the weirdest thing I saw in my (very short) stay there.

I thought the whole paragraph about "This package is never going to be on CRAN" was a warning label :-)

I agree, it works beautifully and performance is ok for desktop use.

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