# independent random variables

## independent random variables

[‚in·də′pen·dənt ¦ran·dəm ‚ver·ē·ə·bəls]
(statistics)
The discrete random variables X1, X2, … , Xn are independent if for arbitrary values x1, x2, … , xn of the variables the probability that X1= x1 and X2= x2, etc., is equal to the product of the probabilities that Xi = xi for i = 1, 2, … , n ; random variables which are unrelated.
References in periodicals archive ?
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  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

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