6.437 Inference and Information


Class Info

Introduction to principles of Bayesian and non-Bayesian statistical inference. Hypothesis testing and parameter estimation, sufficient statistics; exponential families. EM agorithm. Log-loss inference criterion, entropy and model capacity. Kullback-Leibler distance and information geometry. Asymptotic analysis and large deviations theory. Model order estimation; nonparametric statistics. Computational issues and approximation techniques; Monte Carlo methods. Selected special topics such as universal prediction and compression.

This class has 6.008, 6.041B, and 6.436 as prerequisites.

6.437 will be offered this semester (Spring 2018). It is instructed by G. Wornell.

Lecture occurs 9:30 AM to 11:00 AM on Tuesdays and Thursdays in 32-155.

This class counts for a total of 12 credits. This is a graduate-level class.

You can find more information on MIT OpenCourseWare at the Syllabus site or on the 6.437 Stellar site.

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Syllabus
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algorithm inference graphical model

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