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 be offered this semester (Spring 2019). It is instructed by T. Broderick.
Lecture occurs 2:30 PM to 4:00 PM on Tuesdays and Thursdays in 36-153.
This class counts for a total of 12 credits.
You can find more information at the MIT + 6.435 - Google Search site.
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