Transcription of Chapter 6 - Random Processes
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Chapter 6 - Random Processes
EE385 Class Notes 11/11/2014 John Stensby Updates at 6-1 Chapter 6 - Random Processes Recall that a Random variable X is a mapping between the sample space S and the extended real line R+. That is, X : S R+. A Random process ( stochastic process) is a mapping from the sample space into an ensemble of time functions (known as sample functions). To every S, there corresponds a function of time (a sample function) X(t; ). This is illustrated by Figure 6-1. Often, from the notation, we drop the variable , and write just X(t). However, the sample space variable is always there, even if it is not shown explicitly. For a fixed t = t0, the quantity X(t0; ) is a Random variable mapping sample space S into the real line.
Continuous and Discrete Random Processes For a continuous random process, probabilistic variable takes on a continuum of values. For every fixed value t = t0 of time, X(t0; ) is a continuous random variable. Example 6-2: Let random variable A be uniform in [0, 1]. Define the continuous random
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