STATISTICAL SIMULATION

Graduate

This course introduces statistical simulation, and its use as a tool to investigate stochastic phenomena and statistical methods. Topics include the building and validation of stochastic simulation models useful in computing, operations research, engineering and science; related design and estimation problems; variance reduction; and the implementation and the analysis of the results. Graduate students will be required to understand each topic to a greater depth than their undergraduate classmates and will additionally be expected to gain knowledge of Markov Chain Monte Carlo methods. These differentiated expectations will be assessed throughout the term, including a graduate level project on the simulation and applications of Brownian Motion, Stochastic Differential Equations, Markov Chain Monte Carlo methods or other advanced stochastic models or techniques. This course is cross-level listed with STAT 4110. Credit cannot be received for both MCS 8310 and STAT 4110. PREREQUISITE: Admission to a graduate program in Science HOURS OF CREDIT: 3