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 not be offered this semester. It will be available in the Spring semester, and will be instructed by G. W. Wornell and P. Golland.

Lecture occurs 9:30 AM to 11:00 AM on Tuesdays and Thursdays in 54-100.

This class counts for a total of 12 credits.

You can find more information at the MIT + 6.437 - Google Search site or on the 6.437 Stellar site.

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