| Call Number | 14649 |
|---|---|
| Day & Time Location |
TR 11:40am-12:55pm To be announced |
| Points | 4 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Andrew Gelman |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | Prerequisites: STAT GR6102 Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss the relevant theory and get to open questions in model building, computing, evaluation, and expansion. The course is intended for students who want to do applied statistics and also those who are interested in working on statistics research problems. |
| Web Site | Vergil |
| Department | Statistics |
| Enrollment | 2 students (50 max) as of 10:06AM Tuesday, April 21, 2026 |
| Subject | Statistics |
| Number | GR6103 |
| Section | 001 |
| Division | Graduate School of Arts and Sciences |
| Open To | GSAS |
| Note | STAT PhD students only, or by permission. |
| Section key | 20263STAT6103G001 |