There are choices of hypotheses and assumptions about probability distributions.
Good’s choice was the hypothesis family “there are i non-black ravens in the universe”, uniform prior over these, and an assumption that there are N objects in the universe and observations were drawn uniformly at random from these.
For these assumptions, anything that isn’t a non-black raven does carry the same weight for updating the posterior distribution. But the assumptions are obviously false and the hypothesis family doesn’t seem very efficient. I wouldn’t use these by default.
There are choices of hypotheses and assumptions about probability distributions.
Good’s choice was the hypothesis family “there are i non-black ravens in the universe”, uniform prior over these, and an assumption that there are N objects in the universe and observations were drawn uniformly at random from these.
For these assumptions, anything that isn’t a non-black raven does carry the same weight for updating the posterior distribution. But the assumptions are obviously false and the hypothesis family doesn’t seem very efficient. I wouldn’t use these by default.