In this lecture, we review the motivations for stochastic modeling in discrete event system simulation. We also review the basics of probability theory (specifically probability spaces, random variables, probability density functions, probability mass functions, cumulative distribution functions, and moments including expected value (first moment/mean) and variance). We then describe several popular continuous and discrete random variables used in input modeling for stochastic simulation.
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