I found that even where I can parse a technical text (understand all introduced notions, without needing to look up the notions that are used without being defined), it's not a sufficient condition for me being ready for the text. It takes a lot of background effort to build technical fluency that allows to take away a deeper and lasting understanding of a given topic, fluency that isn't required to merely parse the text, or even solve the exercises and ace the exam. Without this fluency, without being prepared, acquired knowledge remains superficial, never becomes very useful, and quickly fades out of memory.
It's like reading a novel in a barely known foreign tongue, translating with a dictionary, and juggling the syntax without feeling the flow of the language. Technically, you can translate everything, but there is no hope for understanding the subtle points of the narrative, and the only way to get there is through obtaining fluency first, and reading the novel later.
What this tells me is that where I can't even parse a text on my own (i.e. there is a non-negligible number of statements I can't understand, or exercises I don't see how to solve), this is an absolutely unambiguous indicator that I'm not ready to try this particular text, and should work on something more elementary.
(This is a strategy for building deep knowledge of a favored subject; it's much more useful to skim in order to obtain superficial general knowledge of many diverse subjects, although elementary textbooks should still be the way to go, not recent research papers.)
The most important parts of a technical book are often the non-technical portions, and this is especially true with Jaynes.
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.