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Prerequisite: Instructor's permission Contents: |
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Overview of basic concepts from probability and statistics concerning random variables, correlation,estimation, confidence intervals, hypothesis testing. Fundamental concepts of System Simulation: Discrete event simulation, Monte Carlo simulation Generation and testing of random numbers. Generation of random variates, random vectors,correlated random variates and stochastic processes. Input modeling; useful probability distributions;hypothesizing families of distributions, estimation of parameters, testing goodness of fit. Building Monte Carlo / Discrete event simulation models of various processes and systems. Use ofgeneral purpose languages such as Python/R/C++ and/or packages such as Anylogic, Flexsim, etc forto build simulation models. Simulation Output data analysis for a single system; statistical analyses for transient systems andsystems in statistical equilibrium. Comparing alternative system configurations; confidence intervals,ranking and selection. Variance reduction techniques. Experimental design, sensitivity analysis andsimulation-based optimization. Optional Topics: Agent based simulation modeling and analysis; System Dynamics Modeling andanalysi |
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