2004
DOI: 10.1002/int.20043
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Probable equivalence, superpower sets, and superconditionals

Abstract: A natural measure of probabilistic equality between sets leads to two measures of probabilistic conditioning that form the endpoints of a conditioning interval. The interval's lower bound is the standard conditional probability or "subconditional" that describes the probability of a subset relation. The upper bound is a new "superconditional" that describes the probability of the corresponding superset relation. These dual conditioning operators correspond to dual set collections and enjoy optimality relations… Show more

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Cited by 9 publications
(6 citation statements)
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“…Concordance between algorithms was measured as A \ B j j = A [ B j j (Kosko, 2004); where A and B are the breakpoint sets returned by each algorithm. Breakpoints belong to the intersection (i.e.…”
Section: Simulated Cgh Datamentioning
confidence: 99%
“…Concordance between algorithms was measured as A \ B j j = A [ B j j (Kosko, 2004); where A and B are the breakpoint sets returned by each algorithm. Breakpoints belong to the intersection (i.e.…”
Section: Simulated Cgh Datamentioning
confidence: 99%
“…One approach is to replace conditional probability with a more general probable equivalence relation. This gives upper and lower conditional probabilities based on the general inequality that P (A)P (B) ≥ P (A ∩ B)P (A ∪ B) [44] because…”
Section: Probabilistic Vs Fuzzy Models Of Causalitymentioning
confidence: 99%
“…We can also interpret e ij in terms of fuzzy subsethood. 25,26 Then e ij states the degree to which the fuzzy concept set C j is a fuzzy or partial subset of fuzzy concept set C j . 7 This abstract framework implies that the edge value e ij is the degree to which the fuzzy concept set C i belongs to the fuzzy power set of fuzzy set C j .…”
Section: Inference With Fuzzy Cognitive Mapsmentioning
confidence: 99%
“…One approach is to replace conditional probability with a more general probable equivalence relation. This gives upper and lower conditional probabilities based on the general inequality that P ( A ) P ( B ) P ( A B ) P ( A B ) 26 because then P ( B | A ) Q ( B | A ) if Q ( B | A ) = P ( B ) / P ( A B ) . But the resulting conditioning interval does not directly address the basic prohibition that lies behind Lewis’s triviality theorem.…”
Section: Inference With Fuzzy Cognitive Mapsmentioning
confidence: 99%