Similarly we can get the distribution function of Y easily from the joint distribution function of X and Y : FY (y) = lim x→∞ (x, y) = F (∞, y). The distribution functions FX and FY are sometimes called the marginal distribution functions of X and Y respectively. The joint distribution function F of X and Y contains all the statistical information about X and Y . In particular, given the joint ...

reliability analysis of rock slope stability has received considerable attention in the literature. it has been used as an effective tool to evaluate uncertainty so prevalent in variables. in this research the application of the jointly distributed random variables method for probabilistic analysis and reliability assessment of rock slope stability with plane sliding is investigated. in a recen...

We construct a class of real-valued nonnegative binary functions on a set of jointly distributed random variables, which satisfy the triangle inequality and vanish at identical arguments (pseudo-quasi-metrics). We apply these functions to the problem of selective probabilistic causality encountered in behavioral sciences and in quantum physics. The problem reduces to that of ascertaining the ex...

A general definition and a criterion (a necessary and sufficient condition) are formulated for an arbitrary set of external factors to selectively influence a corresponding set of random entities (generalized random variables, with values in arbitrary observation spaces), jointly distributed at every treatment (a set of factor values containing precisely one value of each factor). The random en...

Block and Marschak (1960, in Olkin et al. (Eds.), Contributions to probability and statistics (pp. 97-132). Stanford, CA: Stanford Univ. Press) discussed the relationship between a probability distribution over the strict linear rankings on a finite set C and a family of jointly distributed random variables indexed by C. The present paper generalizes the concept of random variable (random utili...