Courses Taught by Timothy Johnson

BIOSTAT682: Applied Bayesian Inference

Graduate level
Residential
Fall term(s) for residential students;
3 credit hour(s) for residential students;
Instructor(s): Timothy Johnson (Residential);
Prerequisites: Biostat 602, Biostat 650 and Biostat 651
Description: Introduction to Bayesian Inference. Bayesian large sample inference, relationship with maximum likelihood. Choice of model, including prior distribution. Bayesian approaches to regression generalized linear models, categorical data, and hierarchical models. Empirical Bayes methods. Comparison with frequentist methods. Bayesian computational methods. Assessment of sensitivity to model assumptions. Emphasis on biomedical applications.
Syllabus for BIOSTAT682

Graduate level
Residential
Winter term(s) for residential students;
3 credit hour(s) for residential students;
Instructor(s): Timothy Johnson (Residential);
Prerequisites: Biostat 602 and Biostat 660
Description: Regression models for the analysis of categorical data: logistic, probit and complementary log-log models for binomial random variables; log-linear models for cross-classifications of counts; regression models for Poisson rates; and multinomial response models for both nominal and ordinal responses. Model specification and interpretation are emphasized, and model criticism, model selection, and statistical inference are cast within the framework of likelihood based inference.
Syllabus for BIOSTAT695