Workshop: Practical Bayesian Computation
Instructor: Fang Chen (SAS Institute Inc.)
This virtual tutorial takes a topic-driven approach that introduces Bayesian simulation, analysis, and illustrates the Bayesian treatment of a wide range of statistical models using SAS software with code explained in detail. The course:
- reviews the fundamentals of Bayesian methods (general paradigm, prior distributions, inferences, multilevel/hierarchical models, etc.)
- covers simulation algorithms (such as MCMC, Metropolis and Hamiltonian Monte Carlo) and convergence diagnosticsv
- introduces two SAS procedures for Bayesian computation
- PROC MCMC for general model building and exploration
- PROC BGLIMM for generalized linear mixed-effects models
- presents Bayesian treatment of various statistical topics, including regression models, multilevel hierarchical models, missing data analysis, model assessment, and predictions. Application areas will focus in agriculture and natural resources, although deviations from this should be expected, given the nature of the presented case studies and examples.
The tutorial emphasizes the practical aspect of performing Bayesian analysis. The objectives are to familiarize statistical programmers and practitioners with the essentials of Bayesian computing, and to equip them with computational tools through a series of worked-out examples that demonstrate sound practices for a variety of statistical models and Bayesian concepts.
Attendees should have a background equivalent to an M.S. in applied statistics. Earlier exposure to Bayesian methods and software is helpful but not necessary. The tutorial is based on SAS/STAT® 15.1.
Attendees who register for the workshop will be given access to the pre-recorded workshop tutorial then will meet on Monday, May 17th from 1:00pm-2:30pm ET via Zoom for live Q&A with the instructor.