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Normalized Conditional Possibility Distributions and Informational Connection Between Fuzzy Variables
Author(s) -
Henri Gwét
Publication year - 1997
Publication title -
international journal of uncertainty fuzziness and knowledge-based systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.333
H-Index - 59
eISSN - 1793-6411
pISSN - 0218-4885
DOI - 10.1142/s0218488597000154
Subject(s) - mathematics , dependency (uml) , fuzzy logic , conditioning , generalization , probabilistic logic , measure (data warehouse) , connection (principal bundle) , conditional probability , fuzzy measure theory , norm (philosophy) , relation (database) , statistics , fuzzy set , artificial intelligence , computer science , data mining , fuzzy number , mathematical analysis , geometry , political science , law
In this article, we introduce a general concept of fuzzy operators. These operators are then used to generalize the possibilistic conditioning formulation proposed by Nguyen (1). This generalization depends on the relation which exists between this conditioning and the probabilistic t-norm. By using other t-norms, other conditionings are obtained, and their properties are studied. One application of normalized possibilistic conditioning is the measure of the dependency between two fuzzy statistical variables. The measure constructed can be considered as the possibilistic counterpart of mutual information commonly used in statistics.

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