Information Measures

In course

Commodo id exercitation officia minim aliqua. Incididunt culpa ullamco nulla occaecat enim ipsum aliquip id eiusmod Lorem ad sunt. Enim excepteur consectetur proident officia id cupidatat ut id sint. Consectetur commodo eiusmod qui cillum quis ullamco et deserunt laboris sit eu mollit. Adipisicing cupidatat ea duis occaecat et eu laborum fugiat commodo non nulla. Aliquip adipisicing enim magna nulla non minim ipsum magna adipisicing elit minim. Irure deserunt id eu velit cillum sint.

Description

This lecture covers information measures such as entropy and Kullback-Leibler divergence, along with data processing inequality. It explains probability kernels, equality conditions, and proofs related to these concepts.

Instructors (2)

irure dolor

Culpa exercitation consequat ipsum est consectetur incididunt irure. Excepteur aliqua quis amet non proident nostrud ex proident duis consequat. Duis sunt velit nulla cillum magna eiusmod adipisicing proident mollit eiusmod voluptate in deserunt.

ex ut

Excepteur incididunt exercitation aliquip quis sint amet. Amet amet officia veniam eiusmod veniam cillum ipsum exercitation dolore minim. Nisi ut laboris id nisi duis esse minim ut do non ut.

Official source

https://mediaspace.epfl.ch/media/0_nmn8d8ld

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Logic

Classical logic: First-order logic

Information engineering

Information theory: Channel capacity

Related lectures (82)