This course reviews the fundamentals of Bayesian methods (prior distributions, inferences, multilevel modeling, and so on), introduces computational techniques (algorithms, convergence, and so on), and emphasizes the practical aspect of performing Bayesian analysis. It introduces the Bayesian treatment of various statistical topics, including regression models, multilevel hierarchical models, missing data analysis, model assessment, and predictions. Other commonly used Bayesian techniques, such as Monte Carlo simulation and use of historical information, are also presented. These techniques and Bayesian applications are illustrated through examples. SAS® software is used for analyses, including the MCMC procedure for general modeling and the specialized BGLIMM procedure for Bayesian generalized mixed models.
Attendees should have a background equivalent to an M.S. in applied statistics. Previous exposure to Bayesian methods and SAS software is useful. Familiarity with material at the level of the textbook Probability and Statistics, by DeGroot and Schervish (Addison Wesley), is appropriate.