[mturmon]

One technical problem with fuzzy logic, that is not suffered by statistics, is that there's no universally-accepted way of combining fuzzy logic truth values.

The rules for manipulation of conditional probabilities, which statistics inherited from probability theory, allow building up complicated statistical models (i.e., complex enough to capture real-world applications) from pieces. This is what the comment by _delirium is saying.

There's no such calculus for the truth values in fuzzy logic. The core problem is, what the concept of "truth value" refers to. In conventional Statistics, probabilities can be grounded in relative frequencies, and in principle measured in real experiments. The same can't be said of a fuzzy truth value.

That much said, there are some people working on generalizations of probability theory to situations where relative frequencies don't make sense, and there's an overlap between the more sophisticated of the fuzzy logic theorists and this community. See, for example, http://www.sipta.org/isipta11/, or http://en.wikipedia.org/wiki/Imprecise_probability

[probably]

Thanks -- didn't realize this thread was still alive. So the showstopper is that there is no equivalent to Bayes theorem for fuzzy logic, you're saying. Great clarification.