|
Abstract:
|
This is an introductory course in Bayesian modeling and computational methods. We will examine the fundamentals of the Bayesian paradigm, including Bayes theorem, deriving posterior distributions, point estimation, interval estimation, hypothesis testing, and model selection. We will discuss Bayesian methods for linear models, generalized linear models, models for longitudinal data, and survival models. Bayesian computational methods also will be discussed, including Gibbs sampling and Metropolis sampling. Various case studies and data sets will be discussed in detail using statistical packages such as SAS, WinBUGS, and R. On the second day, advanced topics will be discussed, including hierarchical modeling, missing data, variable selection, prior elicitation, and Bayesian methods for clinical trial design.
|
