Bayesian Modeling for Biomedical Research & Public Health

SPH BS 849

Graduate Prerequisites: At least one course of statistics to cover principles of probability a nd statistical inference, linear and logistic regression. Knowledge of R. - 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 2025 Schedule

Section Instructor Location Schedule Notes
A1 Doros EVN EB43 F 2:00 pm-4:50 pm First class meeting has occurred, instructor consent required to add prior to second class meeting. If you obtain instructor consent, please submit a SPH Add/Drop Form with the instructor's written permission to the SPH Reg Office.

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