In previous papers, by resorting to the most effective concept of conditional probability, we have been able not only to define fuzzy subsets, but also to introduce in a very natural way the basic continuous T-norms and the relevant dual T-conorms, bound to the former by coherence. Moreover, we have given, as an interesting and fundamental by-product of our approach, a natural interpretation of possibility functions, both from a semantic and a syntactic point of view. In this paper we study the properties of a coherent conditional probability looked on as a general non-additive uncertainty measure of the conditioning events, and we prove that this measure is a capacity if and only if it is a possibility.
CITATION STYLE
Coletti, G., & Scozzafava, R. (2003). Coherent conditional probability as a measure of uncertainty of the relevant conditioning events. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2711, pp. 407–418). Springer Verlag. https://doi.org/10.1007/978-3-540-45062-7_33
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