ABSTRACT
Random variables are used to model experiments which have an outcome which is a single value. This chapter generalizes the concept of a random variable to a random signal or stochastic process. Stochastic processes are used to model experiments which have an outcome which is a signal. The chapter combines the linear systems model with the stochastic process model to show how stochastic processes pass through systems. It then describes two different methods of describing a stochastic process — as a random signal and as an infinite set of random variables. One important parameter used to describe a stochastic process is mean. In general, the mean of a stochastic process will be a function of time. The autocorrelation of a stochastic process is correlation of two random variables, f1, and f2, from the stochastic process. It provides the mutual power of these random variables. The crosscorrelation provides the mutual power between two different stochastic processes as a function of time.
