Propagating possibilistic and probabilistic variables through a mapping yields a fuzzy random variable. We propose a method to attach probability intervals to events pertaining to the output variable. We show that this method is consistent with classical approaches to fuzzy random variables and that the obtained probability interval is the mean value of the fuzzy probability defined by viewing ...

Uncertainty is a key issue in decision analysis and other kinds of applications. Researchers have developed a number of approaches to address computations on uncertain quantities. When doing arithmetic operations on random variables, an important question has to be considered: the dependency relationships among the variables. In practice, we often have partial information about the dependency r...

In statistical inference, the point estimation problem is very crucial and has a wide range of applications. When, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. Therefore, the theory of fuzzy sets plays an important role in formulating such situations. In this paper, we rst recall the crisp uniformly minimum ...

For each natural number d we consider a finite structure Md whose universe is the set of all 0, 1-sequence of length n = 2d, each representing a natural number in the set {0, 1, ..., 2n − 1} in binary form. The operations included in the structure are the constants 0, 1, 2n − 1, n, multiplication and addition modulo 2n, the unary function min{2x, 2n − 1}, the binary functions bx/yc, max(x, y), ...

For given random variables X and Y , the distributions of the product |XY | and the ratio |X/Y | are of interest in many areas of the sciences. In traditional portfolio selection models, certain cases involve the product of random variables. The best examples of this are in the case of investment in a number of different overseas markets. In portfolio diversification models (see, e.g., Grubel [...