Dear Robert, These are some other examples of non-crisp sets.
(1) Rotten apple. After some time an apple become "rotten apple". The question is when. (2) Burning. When the house burn then after that became rubble. The question is, when the house become a rubble? Etc. This is a problem of definition of the words not a number of samples and experiments. Natural language is very imprecise. The discrete set of words cannot describe precisely very complex and continuous phenomena. I don't know what to do with that but I don't think that this is a problem of probability theory. Regards, Andrzej Pownuk http://zeus.polsl.gliwice.pl/~pownuk > > Well, I can't even be sure that the set A includes Robert, since > Robert may not even use a multiplication table to decide on each > question. (He could be autistic and very quickly add the numbers > together each time, never memorizing the table). Bayesians care a lot > less about membership in A than in the probability that Robert will > get the next question right, given he answered the test with X% > accuracy. > > Bob Welch >
