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Lecture 1. Mini-course "Strong probability distances and limit theorems" (Sergey Bobkov)

672 views · Published 31 May 2018 · 2:40:55 · Indexed 21 September 2026

Channel: ФКН ВШЭ · 2018 · Education

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The course explores strong distances in the space of probability distributions, including total variation, relative entropy, chi-squared and more general Renyi/Tsallis informational divergences, as well as relative Fisher information. Special attention is given to the distances from the normal law.

Topics:
- General theory of Renyi and Tsallis informational divergences.
- Relative entropy and chi-squared distance as particular cases.
- Relationship with a total variation. Pinsker-type inequalities.
- Entropy power inequality.
- Basic properties of relative entropy.
- Fisher information.
- Stam’s inequality and its applications. 

Lecturer: Sergey Bobkov, chief research fellow of the International Laboratory of Stochastic Algorithms and High-Dimensional Inference, University of Minnesota.

Faculty of Computer Science: https://cs.hse.ru/en/
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