Lecture

Entropy and KL Divergence

Description

This lecture covers the concepts of entropy and Kullback-Leibler (KL) divergence in the context of probability models. Entropy is a measure of disorder or unpredictability in a random system, while KL divergence quantifies the difference between two probability distributions. The lecture explores how these concepts can be used to compare distributions and make informed decisions in data science. Additionally, it delves into the maximum entropy principle and its application in choosing probability models under specific constraints, providing a theoretical foundation for statistical analysis.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_nxjkrp4q

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Ontological neighbourhood

Information engineering

Information theory: Channel capacity

Statistics

Statistical inference: Mathematical statistics

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