An Introduction to the Theory of Random Signals and Noise by William L. Root Jr.; Wilbur B. Davenport
By William L. Root Jr.; Wilbur B. Davenport
This "bible" of an entire iteration of communications engineers was once initially released in 1958. the point of interest is at the statistical conception underlying the examine of indications and noises in communications platforms, emphasizing recommendations besides s effects. finish of bankruptcy difficulties are provided.Sponsored by:IEEE Communications Society
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Additional resources for An Introduction to the Theory of Random Signals and Noise
3-28), we obtain t: t: and _ _ p(X,y) dy = p(X) (3-29a) __ p(x,Y) dx = p(Y) (3-29b) since here the derivative of an integral with respect to the upper limit is the integrand evaluated at that limit. Eqs. (3-29) are the continuousrandom-variable equivalents of the discrete-random-variable Eqs. (3-9). As with probability distribution functions, the various definitions and results above may be extended to the case of k-dimensional random variables. t Conditional Probability Density Functions. Let us consider now the probability that the random variable y is less than or equal to a particular value Y, subject to the hypothesis that a second random variable :z; has a value falling in the interval (X - I1X < x ~ X).
These aims may be accomplished either by introducing a more general form of integration] or by admitting the use of impulse functions and ignoring the cases in which the continuous part of the distribution does not have a derivative. We shall use the second of these methods. Let us now apply the limiting expression for the probability-density function to a discrete random variable. Suppose, for convenience, that t Specifically the Stieltjes integral. An averaging operation with respect to any probability distribution can be written in terms of Stieltjes integrals even when the continuous part of the probability distribution function does not possess a derivative.
1. Let U8 now apply the above expressions to several functions g(x,y) of interest. First, let g(x,y) == ax + by where a and b are constants. Direct application of Eq. (4-6) gives the result that E(ax + by) := a f _+GOGO t: _ GO xp(x,y) dx dy + b f+1IO _ GO f+- _ GO YP(x,y) dx dy The first term on the right-hand side is a times the statistical average of the function gl (x,1/) =- x, and the second is b times the statistical average of the function g2(X,Y) == y. Thus E(ax + by) =z aE(x) + bE(y) (4-7) That this result is consistent with our original definition, Eq.