JSM 2012 Online Program
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Online Program HomeActivity Details
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| CE_09C | Sun, 7/29/2012, 8:30 AM - 5:00 PM | HQ-Indigo H | |
| Introduction to Bayesian Methods and Software for Data Analysis — Continuing Education Course | |||
| ASA , Section on Bayesian Statistical Science | |||
| Instructor(s): Bradley P Carlin, University of Minnesota, Laura A. Hatfield, Harvard Medical School | |||
| Hierarchical Bayes methods enable combining information from similar and independent experiments, yielding improved inference for individual and shared model characteristics. Bayesian structuring also provides an effective "procedure-generator." With recent advances in computing and the consequent ability to evaluate complex models, Bayesian data analysis methods have gained popularity. This course introduces hierarchical Bayes methods, demonstrates their usefulness in challenging applied settings, and illustrates their implementation using Markov chain Monte Carlo (MCMC). We also provide an introduction to BUGS and other software packages (such as R2WinBugs) for calling BUGS from R. Use of the methods will be demonstrated in advanced settings (e.g., nonlinear longitudinal modeling or spatio-temporal estimation and mapping), where the MCMC Bayesian approach often provides the only feasible alternative that incorporates all relevant model features. Participants should have an MS-level understanding of mathemaical statistics, e.g., at the level of Casella and Berger (2001). Familiarity with common statistical models (e.g., the linear regression model) and computing are assumed, but we do not assume previous exposure to Bayes. The course is aimed at students and practicing statisticians who are intrigued by Bayes and Gibbs, but who may still mistrust the approach as theoretically mysterious and practically cumbersome. | |||
2012 JSM Online Program Home
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