Do you want to become stronger in the way of Bayes? This post is intended for people whose understanding of Bayesian probability theory is currently somewhat tentative (between levels 0 and 1 to use a previous post's terms), and who are interested in developing deeper knowledge through deliberate practice.
Our intention is to form an online self-study group composed of peers, working with the assistance of a facilitator - but not necessarily of a teacher or of an expert in the topic. Some students may be somewhat more advanced along the path, and able to offer assistance to others.
Our first text will be E.T. Jaynes' Probability Theory: The Logic of Science, which can be found in PDF form (in a slightly less polished version than the book edition) here or here.
We will work through the text in sections, at a pace allowing thorough understanding: expect one new section every week, maybe every other week. A brief summary of the currently discussed section will be published as an update to this post, and simultaneously a comment will open the discussion with a few questions, or the statement of an exercise. Please use ROT13 whenever appropriate in your replies.
A first comment below collects intentions to participate. Please reply to this comment only if you are genuinely interested in gaining a better understanding of Bayesian probability and willing to commit to spend a few hours per week reading through the section assigned or doing the exercises.
As a warm-up, participants are encouraged to start in on the book:
Preface
Most of the Preface can be safely skipped. It names the giants on whose shoulders Jaynes stood ("History", "Foundations"), deals briefly with the frequentist vs Bayesian controversy ("Comparisons"), discusses his "Style of Presentation" (and incidentally his distrust of infinite sets), and contains the usual acknowledgements.
One section, "What is 'safe'?", stands out as making several strong points about the use of probability theory. Sample: "new data that we insist on analyzing in terms of old ideas (that is, models which are not questioned) cannot lead us out of the old ideas". (The emphasis is Jaynes'. This has an almost Kuhnian flavor.)
Discussion on the Preface starts with this comment.
I intend to participate, sounds like a great idea!
ETA: I live in Texas, on the northern part of the I-35 corridor. Anyone remotely nearby? (I'll feel lucky to find just one person.)
I live in Plano (i.e., for y'all far away, a bit north of Dallas). I might be interested in participating in a meatspace study group arrangement of some sort. I've never done something like this outside of university classes, dunno how it'd work out, except to guess that it probably depends strongly on individual personalities and schedules and such.
I've studied parts of the Jaynes book in the past. Recently I've been studying more specialized machine learning techniques, like support vector machines, but it seems clear that more time spent studying the more general and fundamental stuff would be time well spent in understanding specialized techniques, and the Jaynes book looks like a good candidate for such study.