I worked my way through the book a month ago. It's very practical and understandable. The ipython notebook format makes it extremely easy to play with the code without worrying about any setup at all. And having it on github made it extremely easy to fix and clarify things as I went.
I think whether it's applicable depends on your job. Two immediate ideas for me were modeling query cost (I work on a high performance server at work, and one of its responsibilities is to guarantee certain rates of queries to certain users) and anomaly detection (for monitoring that server). In my workplace we already have better tools for some of these things, but it's nice to know I could whip up my own if I spent a little time to think about the random variables.
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Are there any plans at all for a dead-tree version, for us old timers? I love what little I've read of this book, but reading on screens just doesn't work for me.
Yes, there are plans. There is currently a PDF version, floating somewhere in the Github repo. This version is out-of-date, and is only a draft. A more physical book may be coming mid-fall though.
I admire the approach and allowing anyone to contribute, but for me to delve into things, I personally prefer something I can take offline and browse and take some notes on...PDF really is the best bet and would be helpful for me in this case...
I have only had success with the stock Android browser for viewing ipython notebooks on a mobile platform. However, for some reason the "A" in P(A) in Chapter 1 does not display. Does anyone know why this would be happening?
This book also serves as excellent guide to ipython/matplotlib visualisation so I would recommend it for this even if you are not interested in primary subject matter.Very well done on all counts.
Does the author have any blog post or plan to write one about this part: "After some recent success of Bayesian methods in machine-learning competitions"? It will then be easier to translate Bayesian Inference to real world problem for noobs like me. I was trained in mathematical Bayesian Inference in an Econometrics class but I never did anything practical with it.
You mention: "trained in mathematical Bayesian Inference in an Econometrics class but I never did anything practical with it" . This book tries to bridge that, from theory to practice. The examples I provide tend to be very practical.
