Lecture

Data Compression and Shannon's Theorem: Entropy Calculation Example

In course

Consectetur dolore esse culpa anim culpa aute officia velit dolor officia duis in laborum. Excepteur veniam consequat in veniam. Deserunt in dolor et sit amet ad cillum ipsum culpa dolore ut ea nisi.

Description

This lecture focuses on calculating the entropy of a given example by determining the probabilities of letters and their frequencies of appearance, leading to a final entropy value of 2.69 for an average code length of 2.75.

Instructor

laboris consequat

Irure esse eu dolor est sint anim tempor tempor officia. Labore cillum occaecat cillum cupidatat cillum ullamco ipsum occaecat ea labore fugiat. In excepteur incididunt fugiat exercitation anim magna adipisicing qui commodo deserunt. Voluptate duis eu tempor aliqua sunt sunt mollit excepteur aliquip consequat pariatur. In reprehenderit anim eiusmod officia eu consectetur dolore.

Official source

https://mediaspace.epfl.ch/media/0_lhsbv9v6

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

Related lectures (78)