Courses Taught by Timothy Johnson

BIOSTAT682: Applied Bayesian Inference

  • Graduate level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Johnson, Timothy
  • 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

BIOSTAT695: Analysis of Categorical Data

  • Graduate level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Johnson, Timothy
  • 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