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In this paper, the concepts of positive dependence and linearlypositive quadrant dependence are introduced for fuzzy random variables. Also,an inequality is obtained for partial sums of linearly positive quadrant depen-dent fuzzy random variables. Moreover, a weak law of large numbers is estab-lished for linearly positive quadrant dependent fuzzy random variables. Weextend some well known inequ...

The unavoidable imprecision of measurements of continuous physical quantities can be modelled by using the concept of fuzzy numbers and fuzzy vectors. Concerning a quantitative usage of such data the classical concept of relative frequencies for real data has to be extended to so-called fuzzy relative frequencies for fuzzy data, whereby the fuzzy relative frequency of a set is a fuzzy number. A...

in applications there occur different forms of uncertainty. the twomost important types are randomness (stochastic variability) and imprecision(fuzziness). in modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. however, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.since many years t...

Fuzziness is discussed in the context of multivalued lo&, and a corresponding view of fuzzy sets is given. Fuzzy random variables are introduced as random variables whose values are not real but fuzzy numbers, and subsequently redefined as a particular kind of fuzzy set. Expectations of fuzzy random variables, characteristic f~cti~ of fuzzy events, probabilities connected to fuzzy random variab...

8 This work deals with the simulation of fuzzy random variables, which can be used 9 to model various realistic situations, where uncertainty is not only present in form 10 of randomness but also in form of imprecision, described by means of fuzzy sets. 11 Utilizing the common arithmetics in the space of all fuzzy sets only induces a conical 12 structure. As a consequence, it is difficult to di...

In practice we are often faced with random experiments whose outcomes are not numbers (or vectors in &I”) but are expressed in inexact linguistic terms. As an example, consider a group of individuals chosen at random who are questioned about the weather on a particular city on a particular winter day. Some possible answers would be “cold,” “more or less cold,” “very cold,” “ extremely cold,” an...