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MATH 640 - Bayesian Statistics |

This course provides a practical introduction to Bayesian statistical methods. It assumes familiarity with concepts in probability theory and statistical inference, as well as some programming experience. Students will learn the fundamentals of Bayesian inference and will be exposed to Monte Carlo simulation methods. The first part of the course will focus on the specification of prior distributions, the evaluation of posterior and predictive distributions, and the theory of Bayesian estimation and hypothesis testing. The second part of the course will focus on Monte Carlo simulation with an emphasis on Markov chain Monte Carlo methods, including the Gibbs sampler and the Metropolis-Hastings algorithm. A variety of statistical models will be considered and illustrated with examples from a wide range of applications. The open source software R and WinBUGS will be used to carry out Bayesian analysis. Text: Bayesian Data Analysis, 3rd Edition, by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin ISBN-10: 1439840954 ISBN-13: 9781439840955 Publisher: CRC Press Prerequisites: Background in probability at the level of MATH-501; familiarity with statistical inference (parameter estimation, hypothesis testing, regression models); some computing experience (familiarity with R or knowledge of a programming language). 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 |

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