About the Conference

Fang Chen

Director of Advanced Statistical Methods, SAS Institute Inc.

Practical Bayesian Data Analysis

This one-day workshop 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. The workshop 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. This workshop will:

• review the fundamentals of Bayesian methods (general paradigm, prior distributions, inferences, multilevel/hierarchical models, and so on)
• cover simulation algorithms (such as MCMC, Metropolis and Hamiltonian Monte Carlo) and convergence diagnostics
• introduce two SAS procedures for Bayesian computation
• PROC MCMC for general model building and exploration
• PROC BGLIMM for generalized linear mixed-effects models
• present 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.

Attendees should have a background equivalent to an M.S. in applied statistics. Previous exposure to Bayesian methods and software is useful but not necessary. The tutorial is based on SAS/STAT® 15.1. There will be no live or hands-on demo, and attendees do not need to bring laptops with SAS pre-installed.

Speaker Biography

Dr. Fang Chen is Director of Advanced Statistical Methods at SAS Institute Inc. Among his responsibilities are development of Bayesian analysis software and the MCMC procedure. He also oversees software development in various mixed models, nonlinear models, causal inferences, survival analysis, Bayesian hierarchical models, and discrete choice models. Before joining SAS, he received his PhD in statistics from Carnegie Mellon University.

Code of Conduct

We are dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of participants in any form. Suggestive statements, sexual innuendo, or offensive remarks are not appropriate during any activity, including during talks, poster sessions, workshops, social functions, after hours parties, via Zoom chat or on Twitter or other online media. Participants asked to stop any harassing behavior are expected to comply immediately. Those violating these rules may be sanctioned or expelled without a refund at the discretion of the organizers. If you are being harassed, notice that someone else is being harassed, or have any other concerns about participant behavior, please notify the organizers immediately.