This three-part tutorial reviews the basic concepts of Bayesian inference and introduces Bayesian computation in SAS. 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 first part of the tutorial provides an introduction to Bayesian inference, covers the fundamentals of prior distributions and concepts in estimation. The tutorial will also cover MCMC methods and related simulation techniques, emphasizing the interpretation of convergence diagnostics in practice.
The second part of the tutorial discusses applications using Bayesian capabilities in SAS/STAT software in the GENMOD, LIFEREG, and PHREG procedures. Examples will include methods such as linear regression, generalized linear models, and survival analysis.
The third part of the tutorial starts with an in-depth introduction to the general simulation procedure PROC MCMC and moves on to demonstrate its use with a series of applications. The presentation takes a topic-driven approach to cover a broad Bayesian topics, such as random-effects models, sensitivity analysis, prediction, PK models, model assessment, and missing data problems.