Includes Bayesian approaches to statistical inference, point and interval estimation using Bayesian and empirical Bayesian methods, representation of beliefs, estimation of the prior distribution, robustness to choice of priors, conjugate analysis, reference analysis, comparison with alternative methods of inference, computational approaches, including Laplace approximation, iterative quadrature, importance sampling, and Markov Chain Monte Carlo (Gibbs sampling). Various applications such as small area estimation, clinical trials and other biomedical applications will be used as examples
Prerequisite: BIOS 510 and BIOS 511.
Semesters Taught: Fall 2018.
Research pertaining to a dissertation and preparing for the proposal.
Semesters Taught: Every Fall and Spring.