BIOSTAT682: Applied Bayesian Inference
- Graduate level
- Fall term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Johnson, Timothy (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