Applied Bayesian Analysis in Public Health

Catalog description

Bayesian analysis is one of the two major statistical paradigms; the other is Frequentist analysis. The course will briefly review the theory behind Bayesian methods and will focus on the practical implementation to public-health and biomedical data. Topics include comparison of Bayesian and Frequentist analyses, Bayesian inference of various one-parameter models and normal models, Markov Chain Monte Carlo algorithms, Bayesian (generalized) linear regression models, and Bayesian hierarchical models. Data analysis with the R software will be emphasized in the course. Upon successful completion of the course, students will be able to formulate Bayesian models for data analysis in public health and biomedicine, and will be able to implement the Bayesian inference using R. Pre-requisites: GPH-GU 3353 and GPH-GU 2184 .