A stochastic process is called discrete-state if [X.sub.t](e) is a discrete random variable for all t [member of] T and continuous-state if [X.sub.t](e) is a

continuous random variable, for all t [member of] T.

A

continuous random variable X is said to have beta distribution of 2nd kind with parameters m and n, if its p.d.f.

As we show here, it can also be used to measure the diversity of an arbitrary probability distribution of a

continuous random variable.

In this paper, we consider network flow systems in which the functional capacities of the arcs are

continuous random variables. The investigated problem is to expand the nominal capacities of arcs so that the system would be capable of securing the demands, while the expansion cost is minimal.

Let the X

continuous random variable with probability function f, H : R [right arrow] R is measurable function on (R, B) and H(X) new random variable.

[tau](x) is a

continuous random variable with probability density function [p.sub.[tau](x)].

then X is a

continuous random variable and f is called its probability density function (pdf).

[18] studied generalization of Tukey's g-h family of distributions, when the standard normal random variable is replaced by a

continuous random variable U with mean 0 and variance 1.

If the set of values that X takes is at most countable, then X is a discrete random variable, if it is an interval in [??], then X is a

continuous random variableAlso it is important to use the odds ratio on probability density function (corresponding to a

continuous random variable).

Roughly speaking,

continuous random variables are found in studies with morphometry, whereas discrete random variables are more common in stereological studies (because they are based on the counts of points and intercepts).

In statistics theory, the mutual information is the one of measures to show the dependence between two

continuous random variables, unlike linear correlation which is not invariant under almost surely strictly monotone functions, Spearman's rho ([[rho].sub.X,Y]), as measures of dependence between two

continuous random variables X and Y, satisfy the following properties: