The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.

Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.

Just started reading this text, and I currently find it very instructive for someone trying to get a handle on Bayesianism from a CS perspective.

I really like the idea here, but think it's important to be more careful about recommendations. There are community members (Gwern, Scott of SSC,) who have done significant research on many areas discussed here, and have fantastic guides to some parts. Instead of compiling a lot of advice, perhaps you could find which things aren't covered well already, link to those that are, and try to investigate others more thoroughly.

Yep! Romeo Stevens also has some very well explained articles here on LW. This one and this one