Dear Hung, Thank you for your illuminating analyses of the Valentina example,and your comments regarding the theory of random fuzzy sets. Your high expertise in both probability theory and fuzzy logic is in evidence.
Regarding the Bayesian approach outlined by Aleks Jakulin, I should like to add the following. First, the conditioning information is perception-based and not quantifiable. Specifically, how would wrinkles, for example, be dealt with? Furthermore, if my perception is that Valentina is young, how would the Bayesian approach apply? If a subjective probability distribution is associated with young, what would be the probability distribution associated with not young? We could apply random sets to this example but fuzzy logic would be much simpler to use. Clearly, the power of probabilistic methods is enhanced when we move from point-valued discrete probability distributions to set-valued discrete probability distributions, that is, to random sets. The power is enhanced further when we move from random sets to random fuzzy sets, as you do. Please note that in my 1979 paper "Fuzzy Sets and Information Granularity," Advances in Fuzzy Set Theory and Applications, M. Gupta, R. Ragade and R. Yager (eds.), 3-18. Amsterdam: North-Holland Publishing Co., 1979 (available upon request), I employed random fuzzy sets to generalize the Dempster-Shafer framework. However, moving from random sets to random fuzzy sets is not sufficient. What has to be done is moving from random fuzzy sets to granule-valued distributions, as described in my paper "Generalized Theory of Uncertainty (GTU)--Principal Concepts and Ideas," in Computational Statistics & Data Analysis 51, 15-46, 2006. Downloadable: http://www.sciencedirect.com/ or available upon request. The concept of a granule is more general than the concept of a fuzzy set. A granule is characterized by a generalized constraint. In my view, this level of generalization is needed to enhance the power of probability theory to a point where it can deal with the examples given in my messages. Try the following: Most Swedes are much taller than most Italians. What is the difference in the average height of Swedes and the average height of Italians? A solution is given in my GTU paper. Theory of random fuzzy sets enriches probability theory but not to a point where it can be said, as you do, that randomness and fuzziness can coexist in the framework of probability theory. Many other problems remain. One of them, as is pointed out in my JSPI paper, which is cited in my previous message, is that in dealing with imprecise probabilities we have to deal in addition with imprecise events, imprecise functions, imprecise relations and other imprecise dependencies. More importantly, a basic problem is that almost all concepts in probability theory are bivalent, e.g, events A and B are either independent or not independent, a process is either stationary or nonstationary, an event either occurs or does not occur, with no shades of truth allowed. In reality, these and other concepts are not bivalent--they are a matter of degree. It may take a long time for this to happen, but I have no doubt that eventually it will be recognized that bivalent logic is not the right kind of logic to serve as a foundation for probability theory. You have made and are continuing to make important contributions to both probability theory and fuzzy logic, and building bridges between them. Please continue to do so. With my warm regards, Lotfi -- Lotfi A. Zadeh Professor in the Graduate School Director, Berkeley Initiative in Soft Computing (BISC) _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai