In this context, in the model studied above, the calculations were carried out according to

random variable with triangular and normal distributions.

The normalized

random variable for MOE had no further adjustment since all the applicable factors in the NDS default to unity.

n]([center dot]) expectation of a

random variable sampled by "number"

The advantage of this approximation is that it consists of terms that clearly are direct effects of the variance of individual

random variables (the first three terms of equation [5] as well as interpretable indirect effects or covariance effects (the later three terms).

The expected value of the

random variable err is zero and the standard deviation is calculated as follows:

Herewith, we assume that there exists a

random variable Z with a zero mean and constants [a.

where the probability is taken over a

random variable X and Y which are uniformly distributed over [{0,1}.

The (optional) quadratic variation of Brownian motion on the interval [0,t] [intersection] T is a

random variable if there exists [bar.

is the expectation of the

random variable X on the interval [a, b] .

1] [B] is element of F we say that the

random variable is defined on the probabilistic space ([OMEGA], F,P) or that a measurable function is defined on the measurable space ([OMEGA], F).

To this aim we introduce definitions of discrete, continuous and absolutely continuous random closed set, coherently with the classical 0-dimensional case, in order to propose an extension of the standard definition of discrete, continuous, and absolutely continuous

random variable, respectively.

In classical statistics, the value of the measurand is assumed to be an unknown constant, often called the true value, and each result of measurement is regarded as a realization of a

random variable with a sampling distribution.