Fuzzy Logic, Informativeness and Bayesian Decision-Making Problems

dc.creatorGolubtsov, P. V.
dc.creatorMoskaliuk, S. S.
dc.date2006-10-28
dc.date.accessioned2026-07-07T07:29:33Z
dc.date.available2026-07-07T07:29:33Z
dc.descriptionThis paper develops a category-theoretic approach to uncertainty, informativeness and decision-making problems. It is based on appropriate first order fuzzy logic in which not only logical connectives but also quantifiers have fuzzy interpretation. It is shown that all fundamental concepts of probability and statistics such as joint distribution, conditional distribution, etc., have meaningful analogs in new context. This approach makes it possible to utilize rich conceptual experience of statistics. Connection with underlying fuzzy logic reveals the logical semantics for fuzzy decision making. Decision-making problems within the framework of IT-categories and generalizes Bayesian approach to decision-making with a prior information are considered. It leads to fuzzy Bayesian approach in decision making and provides methods for construction of optimal strategies.
dc.description41 pages, LaTex, no figures
dc.identifierhttps://arxiv.org/abs/math/0610876
dc.identifierhttp://arxiv.org/abs/math/0610876
dc.identifierHadronic Journal, Vol.26, No.5, pp. 589-630 (2003)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/118155
dc.subjectGeneral Mathematics
dc.subject18D10
dc.titleFuzzy Logic, Informativeness and Bayesian Decision-Making Problems
dc.typetext

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