Earlier quoted context omitted.
Right, this is exactly what I'm getting at - learning a distribution over a fixed sample space can be done with Bayesian methods, or entropy-based methods like the OP suggested, but I'm wondering if there are methods that can automatically adjust the sample space as well. For well-defined mathematical problems like dice rolling and fixed classical mechanics scenarios and such, you don't need this I guess, but for any…
We could discuss completeness of the sample space, and we can also discuss completeness of the hypothesis space . In Solomonoff Induction, which purports to be a theory of universal inductive inference, the "complete hypothesis space" consists of all computable programs (note that all current physical theories are computable, so this hypothesis space is very general). Then induction is performed by keeping all progra…
I mean I think even the "Complexity" coefficient should be uncomputable in general, since you could probably use a program which computes it to upper bound "Complexity", and if there was such an upper bound you could use it to solve the halting problem etc. Haven't worked out the details though!
Would be interesting if there are practical algorithms for this. Either direct approximations to SI or maybe something else entirely that approaches SI in the limit, like a recursive neural-net training scheme? I'll do some digging, thanks!