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BST 604 Applied Bayesian Analysis 3.0 Credits

The course provides a practical introduction to Bayesian statistical inference, which is now at the core of many advanced methods. The course will compare traditional frequentist estimation, which relies on maximization methods, to Bayesian estimation of the posterior distribution. Students will learn numerical integration methods, such as Markov chain Monte Carlo, to obtain these various distributions and ultimately make inference in a Bayesian framework. The course will also use the freely available statistical software packages, R (http://cran.r-project.org/) and WinBUGS (https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/).

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 569 [Min Grade: B] and MATH 510 [Min Grade: B]