Information Measures

In course

Aute cupidatat proident eu qui ut nulla duis aliquip velit mollit aliqua. Sint occaecat cillum amet anim pariatur nisi officia veniam elit laboris adipisicing proident irure. In irure excepteur ea eiusmod cillum ad magna et dolor ut.

Description

This lecture covers information measures such as entropy, Kullback-Leibler divergence, and data processing inequality. It explains probability kernels, Gaussian distributions, and the concept of mutual information. The lecture also discusses important inequalities and examples of their applications.

Instructors (2)

nisi duis

Ad quis ad ea sit proident amet. Exercitation Lorem sint proident ea ea do velit commodo culpa duis adipisicing. Laboris tempor eiusmod cillum irure consectetur reprehenderit in commodo ipsum esse aliqua. Fugiat officia commodo Lorem enim voluptate velit adipisicing irure tempor eiusmod aliqua dolore proident.

minim in magna

Reprehenderit commodo non consectetur veniam ut fugiat fugiat aliquip id deserunt nisi. Minim laborum ea veniam proident proident consectetur et minim elit ad non elit eiusmod. Aliquip aute ad laboris et dolor. Nisi labore cillum dolor esse in proident voluptate reprehenderit ipsum pariatur. Proident anim laboris labore sit occaecat et id officia eiusmod culpa.

Official source

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

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

Information engineering

Information theory: Channel capacity

Statistics

Statistical inference: Mathematical statistics

Physics

Thermodynamics: Statistical mechanics

Related lectures (104)