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An introduction to the uses of simulation and direct computation in analyzing stochastic models and interpreting real phenomena. Deals with generating discrete and continuous random variables, stochastic ordering, the statistical analysis of simulated data, variance reduction techniques, statistical validation techniques, nonstationary Markov chains, and Markov chain Monte Carlo methods. Applications are drawn from problems in finance, manufacturing, and communication networks. Students will be encouraged to program in Python. A precept will be offered to help the students unfamiliar with the language.