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Let X, Y be two independent random variables with associated probability measures (mu,nu) respectively.
Let X, Y be two independent random variables with known distribution having the probability density function (phi_{1}(x)) and (phi_{2} y)), respectively.
Let X, Y be two independent random variables and (Z=f X,Y)) be a function of the random variables X and Y. Then Ebigl sigma^{2}(Z|X bigr)leqsigma^{2} bigl(E Z|Y bigr).
(19) On the other hand, let V and T be two independent random variables such that V is uniformly distributed on ([0,1]) and T has the exponential density (rho _{1} (theta )) defined in (19).
Let and be two independent random sequences and let us further assume that there are no sequencing errors.
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Cochran's theorem [21] states that the sample mean and variance are two independent random variables.
Therefore, if where and are two independent random signals, we have: (19).
Therefore, if where and are two independent random signals, we have: (21) and, after Fourier transform, we obtain: (22) .
hbox {End for} end{array}nonumber end{aligned} (4 where (r_1) and (r_2) are two independent random sequences, (r_1sim U(0,1)) and (r_2 sim U 0,1)).
As described in the proof of Theorem 5, x ̂ 1 and x ̂ 2 are two independent random variables that follow exponential and Chi-squared distributions, respectively.
Therefore, if where and are two independent random signals, we have: (19) and, after Fourier transform, we obtain: (20) (ii) The moment of the product is equal to the product of the moments if the signals are independent.
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