Bayesian Methods for Hackers

A fun and informative book on applied Bayesian modeling in Python.

Assumes knowledge of Python and, honestly, I wouldn't recommend this - alone - as an intro to Bayesian stuff. But if you combine this with Allen Downey's Think Bayes or Khan Academy's Bayes Theorem video or a course (!), you would probably be able to get off the ground with a couple initial models quite quickly.

This book is pitched towards people a bit beyond noob but not total hackers yet on both the PyMC spectrum (Python's most popular (?) Bayesian modeling library... after PySTAN?) and on the Bayesian stats spectrum. For example, the author intentionally hand-waves away Monte Carlo Markov Chain (MCMC) sampling as being much too mathy for this book's purposes, which is... meh, fine. There's a bunch of intuition on what the MCMC sampling is doing and why we use it. (Though I would have introduced conjugate priors earlier, juxtaposing “easy” Bayesian models that can be solved analytically versus hard/annoying models that need sampling.) If you want a bit more mathiness, I thought mathematicalmonk's video was helpful.

There's a good section on multi-armed bandit models (i.e. A/B testing when your sample sizes are super small), which was informative and kinda mind-blowing.

The book is open source and you can read it for free (and make contributions) on their GitHub repo. This is very nice and forward-thinking/Star Trek TNG of them, thank God, but it also means that the later chapters are a bit sloppy. I cloned the repo a couple weeks ago and was gifted with a previously-unseen Chapter 7, which turned out to be a bunch of TODOs. Oh well. There's also some typos, maybe a bit too much Personality (heh), and the nagging feeling that, gosh, I guess I could edit their book for some open source software street cred - but then I thought, WHAT ME BE A GLORIFIED SECRETARY AGAIN NO THANK YOU, and lo, thus dideth the number of women contributors to open source code remaineth at the 5%, the Devil's percent.