Bayesian modelling approaches provide natural ways for researchers in many disciplines to structure their data and knowledge, and they yield direct and intuitive answers to scientific questions (see https://bayesian.org). In this course, students will learn how to construct Bayesian models to relate (potentially complex) data to scientific questions, to fit such models fitting using statistical programs (R, JAGS and/or STAN), to interpret model results and lastly, to check model assumptions. Specific methods covered will include Bayesian linear and logistic regression, as well as hierarchical (regression) models. Additional topics (survival analysis, time series analysis, spline regression models) will be discussed as time allows. The class also includes the discussion of selected papers from the literature that serve as case studies of Bayesian analyses in public health, as well as a project in which students carry out Bayesian modeling for a real data set.
Monday, June 6, 2022 to Monday, September 19, 2022