Course Title: Applied Bayesian Analysis

Course ID: BST228 (Full Term Course)

Topics to be covered:
This course is a practical introduction to the Bayesian analysis of biomedical data. It is an intermediate Master's level course in the philosophy, analytic strategies, implementation, and interpretation of Bayesian data analysis. Specific topics that will be covered include: the Bayesian paradigm; Bayesian analysis of basic models; Bayesian computing: Markov Chain Monte Carlo; STAN R software package for Bayesian data analysis; linear regression; hierarchical regression models; generalized linear models; meta-analysis; models for missing data.

Prerequisites: (BST210 or PHS2000A&B) and BST222

Prior math/stat knowledge: Need to be very comfortable working with probability distributions, evaluating common integrals, and reasoning around basic statistical models (e.g., different types of regression models).

Computing: STAN, R will be used in lecture and lab sessions