Bayesian Modeling for Biomedical Research & Public Health

SPH BS 849

The purpose of this course is to present Bayesian modeling techniques in a variety of data analysis applications, including both hypothesis and data driven modeling. The course will start with an overview of Bayesian principles through simple statistical models that will be used to introduce the concept of marginal and conditional independence, graphical modeling and stochastic computations. The course will proceed with the description of advanced Bayesian methods for estimation of odds and risk in observational studies, multiple regression modeling, loglinear and logistic regression, hierarchical models, and latent class modeling including hidden Markov models and application to model-based clustering. Applications from genetics, genomics, and observational studies will be included. These topics will be taught using real examples, class discussion and critical reading. Students will be asked to analyze real data sets in their homework and final paper.

SPRG 2024 Schedule

Section Instructor Location Schedule Notes
A1 Doros EVN EB43 F 2:00 pm-4:50 pm MED Campus
Class Closed

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.