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MATH 611 - Stochastic Simulation |

This course is an introduction to Monte Carlo methods and computer modeling of stochastic systems. Monte Carlo topics that we will cover include random variable generation, expectation estimation with confidence interval formation, importance sampling, stochastic optimization, MCMC algorithms and sampling of Brownian motion. In developing these topics we will emphasize their role both in statistical inference and modeling. This course will also introduce the students to some fundamental stochastic processes such as discrete state Markov chains, Poisson processes, and Brownian motion; as well as an array of important stochastic models. Computer programming will be a central part of this course. Text: Introducing Monte Carlo Methods with R, by Christian P. Robert and George Casella, Springer Verlag; 2010 edition, ISBN-10: 1441915753. Restrictions: Must be enrolled in one of the following Levels: MN or MC Graduate Must be enrolled in one of the following Majors: Mathematics and Statistics 3.000 Credit hours 3.000 Lecture hours Levels: MN or MC Graduate Schedule Types: Lecture Mathematics Department Restrictions: Must be enrolled in one of the following Levels: MN or MC Graduate Must be enrolled in one of the following Majors: Mathematics and Statistics Prerequisites: MATH 501 and MATH 503 and MATH 510 |

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