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Detailed Course Information


Spring 2021
May 09, 2021
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MATH 628 - Computational Methods in Statistics
Random variable generation, Monte Carlo methods and numerical integration, Bayesian inference and Markov chain Monte Carlo, Metropolis-Hastings and Gibbs Sampling, basics of numerical optimization such as Newton's method, constrained optimization, Expectation-Maximization algorithms. Prerequisite: MATH 620 or DSCI 602 or permission of the department chairperson.
3.000 Credit hours
3.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture, Online Fixed Times (Synch), Online (Asynchronous), Immersive Learning, Study Abroad, Societal Issue/Global Chal

Mathematical Sciences Department

Must be enrolled in one of the following Levels:     

Graduate level MATH 620 Minimum Grade of D-

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