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 thought underlying the examine of signs and noises in communications structures, emphasizing options in addition s effects. finish of bankruptcy difficulties are provided.Sponsored by:IEEE Communications Society

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Extra resources for An Introduction to the Theory of Random Signals and Noise

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Is) to contain impulse functions. The statistical average of y = g(x) can also be expressed in terms of probabilities on the sample space of y; if the probability density Pt(Y) is defined, then E[g(:t)] = E(y) = J-+: YP2(Y) dy (4-4) t Also known &8 the mean, mathematical ezpectGtitm, ,tocMstic average, and maemble For convenience, we will sometimes denote the statistical average by a bar over the quantity beinl averaged, thus: g(z). "tJgI. 47 AVERAGES Multiple Random Variables. So far we have studied only statistical averages of functions of a single random variable.

00 ~ z S + 00). Consider a point X on the real line. The function of X whose value is the probability Pi» ~ X) that the random variable x is less than or equal to X is called the probability distribution junction of the random variable z. Since probabilities are always bounded by zero and one, the extremal values of the probability distribution function must also be zero and one: t The notation, t The use of the I SA means that the point' is an element of the point set SA. term "random variable" for a fUDction is dictated by tradition.

The graph of such a probability distribution function has the form of a staircase, as shown in Fig. 3-lb. We have shown above that we may extend the concept of the probability density function to cover the case of the discrete random variable by admitting the use of impulse functions. Henceforth we will use, therefore, the probability density function, if convenient, whether we are concerned with continuous, discrete, or mixed random variables. Joint Probability Density Functions. In the case of a single random variable, the probability density function was defined as the derivative of the probability distribution function.

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