Statistics 220 - Bayesian Data Analysis
Instructor: Mark Irwin
Begins with basic Bayesian models, whose answers often appear similar to classical answers, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of conclusions to change in models. Throughout, emphasis on drawing inferences via computer simulation rather than mathematical analysis.
There will now be regular sections for the course. They will start Tuesday, March 22 from 5:00 - 6:00 in SC 222. The location and time may change after spring break.
Given that I didn't get to where I wanted to be last Thursday, the due date for the last assignment is now Tuesday, May 10th.