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Basic information
| Instructor | Lawrence Joseph |
| Credits | 3 |
| Course Objectives and Topics Covered | To provide researchers with an introduction to practical Bayesian methods. Topics will include Bayesian philosophy, simple and more complex models, linear and logistic regression, hierarchical models, diagnostic tests, sample size methods, issues in clinical trials, measurement error and missing data problems. Numerical techniques including Monte Carlo integration, sampling importance resampling (SIR), and the Gibbs sampler will be covered, including programming in R and WinBUGS. While all examples will be to epidemiological research, most of the ideas and material will be applicable to other areas of research. |
| Time | Mondays and Wednesdays 11 AM - 12:30 PM (Jan 9 - April 16). |
| Place | Mondays: Purvis Hall Room 24
Wednesdays: Purvis Hall Room 24 except for January 25, February 15, February 29, March 7, March 19, March 21, April 4, April 11, Room 25. |
| Assessment | Ten asignments, worth 10% each. There will be no midterm or final exams. |
| Assignment due dates | | 1 | | 2 | | 3 | | 4 | | 5 | | 6 | | 7 | | 8 | | 9 | | 10 | January 25, 2012
| | February 1, 2012
| | February 8, 2012
| | February 27, 2012
| | March 5, 2012
| | March 12, 2012
| | March 21, 2012
| | April 2 2012
| | April 11, 2012
| | April 16, 2012
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| Textbook | Andrew Gelman, John Carlin, Hal Stern and Donald Rubin. Bayesian Data Analysis, 2nd Edition, Chapman and Hall, 2003. Textbook review of Gelman et al |
| Prerequisites | EPIB-607 and EPIB-621, or equivalent. |
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