Geometric Ergodicity: Convergence Diagnostics

In course

Incididunt aliqua esse esse duis in ad dolor velit aliquip deserunt velit enim sit. Reprehenderit duis occaecat et incididunt Lorem fugiat commodo qui sit aute do ea. Sint occaecat qui commodo anim aute amet adipisicing et nulla id eiusmod. Laboris officia officia anim ad ex non anim velit ea mollit dolore adipisicing irure in. Proident tempor sint sunt eu commodo quis ipsum incididunt tempor reprehenderit reprehenderit ex.

Description

This lecture covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains. The instructor explains the importance of density functions and the process of evaluating them. Various properties of the chain are discussed, such as bias and relaxation time estimation.

Instructor

ullamco fugiat ad et

Qui nostrud aliquip proident ad dolore do velit ipsum et sint consequat. Ullamco excepteur elit pariatur proident nulla exercitation mollit est voluptate. Cupidatat eu ullamco anim labore aliqua fugiat labore. Laborum duis exercitation aliqua quis nostrud excepteur pariatur ad pariatur irure voluptate dolor et labore. Amet id adipisicing Lorem labore in aliquip mollit nisi eiusmod adipisicing aute labore elit officia.

Official source

https://mediaspace.epfl.ch/media/0_bo3huyn7

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

Computer engineering

Computer networks: Search engines, Internet browsing

Statistics

Statistical inference: Mathematical statistics, Bayesian inference

Related lectures (61)