Narrow Results

Using the concept of triangular norm, we define T-fuzzy subalgebraic hypersystems, we examine a number of extended uncertainty algebraic hypersystems and study a few results in this respect. In fact, we define a probabilistic version of algebraic hypersystems using random sets. We show that fuzzy algebraic hypersystems defined in triangular norms are consequences of probabilistic algebraic hypersystems under certain conditions.

In some probabilistic problems, complete information about the probability model may not exist. In this article, we obtain a lower and upper probability for an arbitrary event by using rough set theory and then a measurement for inclusiveness of events is introduced.

A possibility to define a binary operation over the space of pairs of belief functions, inverse or dual to the well-known Dempster combination rule in the same sense in which substraction is dual with respect to the addition operation in the space of real numbers, can be taken as an important problem for the purely algebraic as well as from the application point of view. Or, it offers a way how to eliminate the modification of a belief function obtained when combining this original belief function with other pieces of information, later proved not to be reliable. In the space of classical belief functions definable by set-valued (generalized) random variables defined on a probability space, the invertibility problem for belief functions, resulting from the above mentioned problem of "dual" combination rule, can be proved to be unsolvable up to trivial cases. However, when generalizing the notion of belief functions in such a way that probability space is replaced by more general measurable space with signed measure, inverse belief functions can be defined for a large class of belief functions generalized in the corresponding way. "Dual" combination rule is then defined by the application of the Dempster rule to the inverse belief functions.

In order to improve the computational efficiency of the response surface method (RSM) in analysis of structural reliability, a full-space response surface method (FRSM) is presented in this paper. First, a vector-type response surface is developed by expanding the stochastic nodal displacement vector along the Krylov basis vectors defined by the global stiffness matrix and the force vector. Then the effective collocation points are picked out of the candidate ones according to the linear independence of the combining row vector. Finally, the unknown coefficients of the proposed response surface are determined by means of the regression analysis. Examples show that the proposed FRSM requires much fewer effective collocation points and less times of finite element analysis, achieving much higher computational efficiency by comparing with the traditional RSM.

Probability theory is the foundation of statistical science, providing a mathematical means of modeling random experiments or uncertainty. Through these mathematical models, researchers are able to draw inferences about the random experiments using observed data. The aim of this chapter is to outline the basic ideas of probability theory that are fundamental to the study of statistics. The entire structure of probability, and therefore of statistics, can be built on the relatively straightforward foundation given in this chapter.