IEORE3404 Simulation Modeling and Analysis


Prerequisite(s): SIEO W3600 or SIEO W4150: Introduction to Probability and Statistics, computer programming language such as C, C++, Java or Matlab.

Corequisite(s): IEOR E3106 or IEOR E4106: Stochastic Models

This is an introductory course to simulation, a statistical sampling technique that uses the power of computers to study complex stochastic systems when analytical or numerical techniques do not suffice. The course focuses on discrete-event simulation, a general technique used to analyze a model over time and determine the relevant quantities of interest. Topics covered in the course include the generation of random numbers, sampling from given distributions, simulation of discrete-event systems, output analysis, variance reduction techniques, goodness of fit tests, and the selection of input distributions. The first half of the course is oriented towards the design and implementation of algorithms, while the second half is more theoretical in nature and relies heavily on material covered in prior probability courses. The teaching methodology consists on lectures, recitations, weekly homework, and both in-class and take-home exams. Homework almost always includes a programming component for which students are encouraged to work in teams. Students who have taken IEOR E4703 Monte Carlo simulation may not register for this course for credit.

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