stat_rethinking_2022

I've been working through the Statistical Rethinking[0] course with Julia recently and so far the language has been really intuitive, same for the libraries (Turing, Distributions). And it's nice that you don't have to vectorize your code and can use normal for-loops. Also, the way dynamic dispatch + specialization work is beautiful. [0]:https://github.com/rmcelreath/stat_rethinking_2022

Looks really promising, will give it a read through! For those looking for an easier entry into Bayesian analysis, as a related topic and a starting place before embarking on Bayesian optimization, I would highly recommend "Statistical Rethinking" by Richard McElreath: https://xcelab.net/rm/statistical-rethinking/. Why I really like Richard's book is that it bypasses lot of the heavy mathematical/integral work, and goes straight into sampling - from my experience, you generally can't do integrals by hand, you describe your model in terms of a hierarchy of probability distributions and let an MCMC sampler take care of the rest. Richard's book touches upon causality (important and often overlooked topic in ML!), and you can follow his course online: https://github.com/rmcelreath/stat_rethinking_2022 EDIT: made my recommendation more explici