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 topics such as universal inference and learning, and universal features and neural networks.

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

6.437 will be offered this semester (Spring 2019). It is instructed by P. Golland and G. W. 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.

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