6.435 Bayesian Modeling and Inference
Covers Bayesian modeling and inference at an advanced graduate level. Topics include de Finetti's theorem, decision theory, approximate inference (modern approaches and analysis of Monte Carlo, variational inference, etc.), hierarchical modeling, (continuous and discrete) nonparametric Bayesian approaches, sensitivity and robustness, and evaluation.
6.435 will not be offered this semester. It will be available in the Spring semester, and will be instructed by T. Broderick.
Lecture occurs 2:30 PM to 4:00 PM on Tuesdays and Thursdays in 24-121.
This class counts for a total of 12 credits.
You can find more information at the 6.435 - Theory of Learning and System Identification - Spring 2007 site.
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