This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
|CE_06C||Sun, 8/1/2010, 8:00 AM - 12:00 PM||CC-2&3 (East)|
|Practical Bayesian Computation — Continuing Education Course|
|Section for Statistical Programmers and Analysts|
|Instructor(s): Fang Chen, SAS Institute|
|This half-day course reviews the basic concepts of Bayesian inference and focuses on the practical use of Bayesian computational methods. The objectives are to familiarize statistical programmers and practitioners with the essential s of Bayesian computing, and to equip them with computational tools through a series of worked-out examples that demonstrate sound practice for a variety of statistical models and data analysis problems. The first part of the course will review differences between classical and Bayesian approaches, fundamentals of prior distributions, concepts of estimation, prediction, sensitivity analysis and model selection. The simulation methods covered will include Markov chain Monte Carlo methods (Metropolis and Gibbs samplers) and rejection sampling, emphasizing the interpretation of convergence diagnostics in practice. The second part will illustrate the Bayesian treatment of linear and nonlinear regression, generalized linear models, survival analysis (Cox regression and piecewise exponential model), zero-inflated Poisson models, mixture models, and hierarchical models. Additional examples will demonstrate how to perform sensitivity analysis, make inference on functions of parameters, carry out predictions, and use historical data in Bayesian modeling. The examples will be done using SAS. As a prerequisite, attendees should have a background in probability and statistics equivalent to an M.S. in applied statistics. Previous exposure to Bayesian methods is useful but not required.|