Getting Started with Bayesian Analysis

This presentation introduces the audience to the realm of Bayesian analyses and concepts. Participants will be able to see the difference between the Bayesian approach and the classical approach to statistics. Convergence diagnostics, images, and a sample example of PROC MCMC will be shared.

Missing Data in PROC MCMC

This presentation will show the participants how to incorporate missing data into the Bayesian analysis and not be subjected to complete case analysis. Posterior distributions for the missing values will be generated and the uncertainty of the missing will be captured within the final model.

Bayesian Generalized Linear Mixed Models

This presentation will feature the new BGLIMM procedure available in SAS/STAT 15.1. This will allow the participant to model non-normal responses and include random effects within their Bayesian approach.

About the Presenter

Danny Modlin has been an Analytical Training Consultant at SAS since April 2011. Before SAS, he was a teacher in middle school and high school, as well as Teaching Assistant at the University of North Carolina at Wilmington and North Carolina State University. He has a Bachelors of Science in Mathematics from Elon College, a Masters of Mathematics from UNCW, and a Masters of Statistics from NCSU.