In probability theory, a log-normal distribution is a

continuous probability distribution of a random variable whose logarithm is normally distributed.

Heitsch and Romisch [15] proposed a kind of fast forward and backward reduction to reduce the computational complexity, applying the discretization of possibility distribution instead of the initial

continuous probability distribution. A scenario optimal reduction technique, introduced by Dupacova et al [14], applied the Foret-Mourier distance and duality theory to compute the distance between two probability measures.

In 1977, Muth [25] introduced a

continuous probability distribution in the context of reliability theory.

The seller has overall information about rival prices represented by a

continuous probability distribution F, which is nondegenerate.

(2001) proposed a method based on the reduced

continuous probability distribution (NCRIz).

In probability theory and statistics the Weibull distribution is a

continuous probability distribution. It is named after the Waloddi Weibull.

The Lognormal distribution ln N([mu], [[sigma].sup.2]) is a

continuous probability distribution of random variable, whose logarithm is normally distributed.

If we think of X as being drawn from a

continuous probability distribution, then the probability of having X equal exactly 1 is 0.

The von Mises distribution is a

continuous probability distribution on the circle, and is used in applications of directional statistics such as grain orientation.

Hence a

continuous probability distribution function (pdf) becomes a discrete pdf that can be written:

With a

continuous probability distribution an infinite number of possible outcomes exist.

Buyers and sellers receive valuations of securities to be exchanged drawn from a

continuous probability distribution f(x) defined over [[x.bar], [bar.x]] [epsilon] [R.sup.+] and its cumulative density function [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] f(x)dx common to all sellers and buyers.