As I understand it, frequentism requires large numbers of events for its interpretation of probability, whereas the bayesian interpretation allows the convergence of relative frequencies with probabilities but claims that probability is a meaningful concept when applied to unique events, as a "degree of plausibility".
Do you (or anyone else reading this) know of any attempts to give a precise non-frequentist interpretation of the exact numerical values of Bayesian probabilities? What I mean is someone trying to give a precise meaning to the claim that the "degree of plausibility" of a hypothesis (or prediction or whatever) is, say, 0.98, which wouldn't boil down to the frequentist observation that relative to some reference class, it would be right 98/100 of the time, as in the above quoted example.
Or to put it in a way that might perhaps be clearer, suppose ...
To whom it may concern:
This thread is for the discussion of Less Wrong topics that have not appeared in recent posts. If a discussion gets unwieldy, celebrate by turning it into a top-level post.
(After the critical success of part II, and the strong box office sales of part III in spite of mixed reviews, will part IV finally see the June Open Thread jump the shark?)