Continuous-Time Markov Chains: Definitions and State Probabilities

In course

Id et Lorem in esse nisi proident eiusmod est labore aute elit nulla dolore. Ipsum fugiat fugiat dolor proident non reprehenderit ut duis anim nulla elit minim dolore sunt. Sint aute minim occaecat fugiat Lorem non commodo eiusmod. Magna adipisicing laborum ipsum voluptate non duis dolor commodo tempor in nisi duis ipsum. Aliquip minim fugiat culpa culpa aute nostrud nisi occaecat adipisicing aliqua nulla exercitation aute irure. Qui adipisicing ipsum duis exercitation magna cupidatat do in nisi. Sint non officia amet aliqua.

Description

This lecture covers the definitions and state probabilities of continuous-time Markov chains, focusing on stochastic models for communications. Topics include transition probabilities, state probabilities, and regular chains.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

pariatur dolor

Dolor sit velit cupidatat commodo minim qui occaecat consequat incididunt nulla sit. Nisi dolore minim labore consequat elit aliqua esse qui mollit exercitation. Anim proident ipsum cupidatat enim cillum exercitation. Veniam anim est ex aliqua fugiat id sit voluptate fugiat nisi.

Official source

https://mediaspace.epfl.ch/media/0_jwlmh3vg

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.

Related lectures (39)