I think one important thing to keep in mind when assigning prior probabilities to yes/no questions is that the probabilities you assign should at least satisfy the axioms of probability. For example, you should definitely not end up assigning equal probabilities to the following three events -

1. Strigli wins the game.
2. It rains immediately after the match is over.
3. Strigli wins the game AND it rains immediately after the match is over.

I am not sure if your scheme ensures that this does not happen.

Also, to me, Bayesianism sounds like an iterative way of forming consistent beliefs, where in each step you gather some evidence and update your probability estimates for the truth or falsity of various hypotheses accordingly. But I don't understand how exactly to start. Or in other words, consider the very first iteration of this whole process, where you do not have any evidence whatsoever. What probabilities do you assign to the truth or falsity of different hypotheses?

One way I can imagine is to assign all of them a probability inversely proportional to their Kolmogorov complexities. The good thing about Kolmogorov complexity is that it satisfies the axioms of probability. But I have only seen it defined for strings and such. I don't know how to define Kolmogorov complexity of complicated things like hypotheses. Also, even if there is a way to define it, I can't completely convince myself that it gives a correct prior probability.