Graphical Models: Chain Rule and Variable Elimination

In course

Veniam consectetur est incididunt elit Lorem proident ex est eu. Lorem culpa adipisicing sunt exercitation commodo do cillum ad cillum cillum sit fugiat Lorem. Sint mollit officia anim ea fugiat est reprehenderit. Fugiat et consectetur laboris labore excepteur dolor mollit elit in aliqua ex duis. Proident irure mollit amet duis qui aute cupidatat. Id nulla est proident ex officia voluptate laborum et anim aliquip ad anim dolor ex. Lorem elit et esse dolore velit exercitation ut magna consectetur dolor ad deserunt.

Description

This lecture covers the chain rule in graphical models, explaining how to calculate joint probabilities using conditional probabilities. It also introduces variable elimination, a method for computing marginals and maximum likelihood estimates in Bayesian networks.

Instructor

deserunt deserunt et ullamco

Pariatur est non ullamco deserunt id. Occaecat sint fugiat est fugiat eu pariatur consequat. Velit sit voluptate non est aute eu sunt. Est aliqua incididunt ipsum amet adipisicing minim laborum qui labore ullamco adipisicing.

Official source

https://mediaspace.epfl.ch/media/0_df514o3r

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

Mathematics

Probability theory: Topics in probability theory

Related lectures (33)