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 ?
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.
0] we have [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the sum of m independent random variables which have all the same distribution as [S.
The selections are his first papers on probability 1924-36, the interaction with Paul Levy, infinitely divisible distributions, his book on the distribution of the sum of independent random variables, teaching probability theory and analysis, and last papers on probability 1950-59.
Discriminant analysis is a technique for classifying and separating individuals into different groups (dependent variables) based on the set quantitative independent random variables.
Ambagaspitiya(1998) considered a general method of constructing a vector of p dependent claim numbers from a vector of independent random variables, derived formulae to get the correlated claims distribution.
Specific topics described include injectivity of the Dubins-Freedman construction of random distributions, almost sure convergence of weighted sums of independent random variables, aperiodic order via dynamical systems, laws of iterated logarithm for weighted sums of iid random variables, and homeomorphic Bernoulli trial measures and ergodic theory.
R], it is easy to show that p and co are independent random variables with
l] are realizations of independent random variables with mean zero and finite variance.
K] [less than or equal to] x, the distribution function of the sum of k independent random variables with the same distribution as U.
If X ~ HCM and Y ~ HCM are independent random variables, then XY ~ HCM, X/Y ~ HCM, and [X.
2] independent random variables with uniform distribution in the interval of (0, 1).

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