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Fuzzy Sets and Systems, 1981
Abstract In [4] Hohle has defined fuzzy measures on G-fuzzy sets [2] where G stands for a regular Boolean algebra. Consequently, since the unit interval is not complemented, fuzzy sets in the sense of Zadeh [8] do not fit in this framework in a straightforward manner.
Klement, Erich Peter +2 more
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Abstract In [4] Hohle has defined fuzzy measures on G-fuzzy sets [2] where G stands for a regular Boolean algebra. Consequently, since the unit interval is not complemented, fuzzy sets in the sense of Zadeh [8] do not fit in this framework in a straightforward manner.
Klement, Erich Peter +2 more
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Information Sciences, 1984
We discuss some relationships between probability theory and statistics on one hand, and the theory of fuzzy sets on the other hand. We develop various statistical techniques for the analysis of imprecise data and for inference based on imprecise data.
Ralescu, Anca L., Ralescu, Dan A.
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We discuss some relationships between probability theory and statistics on one hand, and the theory of fuzzy sets on the other hand. We develop various statistical techniques for the analysis of imprecise data and for inference based on imprecise data.
Ralescu, Anca L., Ralescu, Dan A.
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Fuzzy Statistics and Fuzzy Probability
2018In the objective world, randomness and fuzziness coexist inside many things. Although fuzzy mathematics and probability theory cannot replace each other, they can infiltrate into each other. The fuzziness is introduced into stochastic phenomena in this chapter. Simple fuzzy statistics and fuzzy probability will be discussed.
Hao-Ran Lin +2 more
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Intuitionistic Fuzzy Probability
2010Fuzzy Probabilities are an extension of the concept of probabilities with application in several practical problems. The former are probabilities represented through fuzzy numbers, to indicate the uncertainty in the value assigned to a probability. Moreover, Krassimir Atanassov in 1983 added an extra degree of uncertainty to classic fuzzy sets for ...
Claudilene Gomes Da Costa +2 more
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Fuzzy probability over fuzzy -field with fuzzy topological spaces
Fuzzy Sets and Systems, 2000In the classical paper [J. Math. Anal. Appl. 23, 421-427 (1968; Zbl 0174.49002)] \textit{L. A. Zadeh} defined a probability of a fuzzy event \(\mu:\Omega\to [0,1]\) as \(m(\mu)=\int \mu dP\), where \((\Omega,\mathcal{F},P)\) is a classical probability space. Since then many authors have generalized this definition into different directions.
Chiang, Jershan, Yao, Jing-Shing
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2019
Human health risk assessment is an important and a popular aid in the decision-making process. The basic objective of risk assessment is to assess the severity and likelihood of impairment to human health from exposure to a substance or activity that under plausible circumstances can cause harm to human health. One of the most important aspects of risk
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Human health risk assessment is an important and a popular aid in the decision-making process. The basic objective of risk assessment is to assess the severity and likelihood of impairment to human health from exposure to a substance or activity that under plausible circumstances can cause harm to human health. One of the most important aspects of risk
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Complexity of Fuzzy Probability Logics
Fundamenta Informaticae, 2001The satisfiability problem for the logic FP(Ł) (fuzzy probability logic over Łukasiewicz logic) is shown to be NP-complete; satisfiability in FP(ŁΠ) (the same over the logic joining Łukasiewicz and product logic) is shown to be in PSPACE.
Hájek, Petr, Tulipani, Sauro
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Fuzzy Sets and Systems, 1985
Several recent papers have discussed an extension of decision analysis: both the rewards and probabilities are modelled using fuzzy sets. However, arbitrary specification of the membership functions of the fuzzy probabilities in these models may lead to internal inconsistencies. To avoid this, the fuzzy beta possibility distribution is introduced.
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Several recent papers have discussed an extension of decision analysis: both the rewards and probabilities are modelled using fuzzy sets. However, arbitrary specification of the membership functions of the fuzzy probabilities in these models may lead to internal inconsistencies. To avoid this, the fuzzy beta possibility distribution is introduced.
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Conditional probability and fuzzy information
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
COLETTI, Giulianella, SCOZZAFAVA R.
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International Journal of General Systems, 1990
Fuzziness is explored as an alternative to randomness for describing uncertainty. The new sets-as-points geometric view of fuzzy sets is developed. This view identifies a fuzzy set with a point in a unit hypercube and a nonfuzzy set with a vertex of the cube.
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Fuzziness is explored as an alternative to randomness for describing uncertainty. The new sets-as-points geometric view of fuzzy sets is developed. This view identifies a fuzzy set with a point in a unit hypercube and a nonfuzzy set with a vertex of the cube.
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