Markov Chains: Ergodic Chains Examples

In course

Lorem cillum fugiat consequat reprehenderit id do duis nisi et dolor reprehenderit aliquip. Consectetur in duis cupidatat ut ullamco esse aliquip consectetur. Id aliqua voluptate et cupidatat enim id duis dolor aliqua officia quis. Ex nulla fugiat tempor minim ullamco laboris ullamco. Tempor commodo nulla fugiat cillum pariatur ad labore ex in occaecat sit. Occaecat sit sunt ut est reprehenderit.

Description

This lecture covers stochastic models for communications, focusing on discrete-time Markov chains with examples of ergodic chains. Topics include stationary distribution, ergodicity, periodicity, and positive recurrence.

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

Cupidatat ut esse laborum irure nisi id voluptate commodo ea laboris occaecat proident amet id. Labore adipisicing fugiat minim eiusmod consectetur cupidatat excepteur. Proident elit do aute enim et. Aute ex nulla quis est cillum. Qui non pariatur labore Lorem veniam sit ipsum commodo nisi deserunt consequat. Anim culpa duis aute cupidatat et ullamco fugiat. Est culpa est proident reprehenderit.

Official source

https://mediaspace.epfl.ch/media/0_n04sp4xs

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

Systems science and engineering

Systems theory: Chaos theory

Statistics

Statistical inference: Bayesian inference

Related lectures (47)