Saddlepoint Approximation for the Distribution of the Sum of

Independent Random Variables," Advances in Applied Probability, 475-490, 1980.

2] are two

independent random variables of uniform distribution over [0, 1].

The Central Limit Theorem, a classical result in probability theory, tells us that the distribution of a sum of several

independent random variables tends towards Gaussian distribution, under certain conditions.

n] is a discrete structure, such as a permutation or a graph, and where the input values are realizations of

independent random variables with the same distribution, the output sequence is a Markov chain X = ([X.

2]) represents a white noise, a series of

independent random variables, identically distributed: E[[epsilon].

2](x) of two

independent random variables with Gaussian distribution has also Gaussian distribution

Shao [21] proved a comparison theorem on maximal inequalities between negatively associated and

independent random variables, and obtained the Rosenthal-typemaximal inequality and the Kolmogorov exponential inequality.

It omits materialaon probability and probability distributions,

independent random variables, weighted sums of random variables, fitting a probability distribution to data with @RISK, one-way ANOVA (discussed online), theapartial F test, and auto regression models.

In the end, we derive results on products and quotients of

independent random variables.

Table 6 presents the reliability index [beta] regarding different values of the coefficient of variation for

independent random variables [E.

k] ([omega])'s are real and

independent random variables with mean zero and variance one.

We further assume, that ti delays are

independent random variables, hence (by the law of large numbers--(Ventsel, 1969)) the sum (10) is a normally distributed random variable with parameters