Bayesian Time Series in PROC MCMC

This presentation incorporates time series components (autoregressive, seasonal, and exogenous) into models using a Bayesian approach in PROC MCMC. Using the benefits of indexing, participants are shown how to easily reference lagged and next values from the series. Presentation ends with a discussion about posterior predictive distributions (Bayesian scoring).

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.

About the Presenter

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