Biostatistics 249 -- Bayesian Methodology in Biostatistics
Course Description: Bayes theorem, decision theory, general principles (likelihood, exchangeability, de Finetti’s theorem), prior distributions, inference (exact, normal approximations, non-normal approximations), computation (Monte Carlo, convergence diagnostics), model diagnostics (Bayes factors, predictive ordinates), design, empirical Bayes methods.

This class is at the Graduate level
Instructor: Christopher J. Paciorek (Public Health)
Prerequisites: Biostatistics 231 and Biostatistics 232, or signature of instructor required.

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