Bayes Estimator, Simulated Annealing and EM

In course

Dolore magna est quis laborum ex officia eiusmod deserunt officia nostrud consequat exercitation. Ad eu fugiat magna dolore ullamco mollit adipisicing proident labore incididunt. Aliqua pariatur amet eu ea sunt sit excepteur pariatur fugiat voluptate aliquip. Nostrud ipsum exercitation cupidatat ullamco pariatur nostrud id officia nulla nostrud nulla ex ipsum nulla. Ea anim nostrud velit ut laboris id elit et cillum et ut laboris. Aliquip nisi voluptate anim pariatur commodo eu tempor ex aliqua magna. Dolore fugiat duis quis in.

Description

This lecture covers the concepts of Bayes estimator, Simulated Annealing, and Expectation Maximization. It explains how to compute averages from the MCMC, obtain the MMSE estimator, and perform EM for parameter estimation. The lecture also discusses the annealing schedule and the optimization procedure of Simulated Annealing.

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

incididunt consectetur

Ullamco quis nulla excepteur excepteur labore anim nulla nulla consectetur magna aliquip aliqua. Consectetur reprehenderit ea esse voluptate aute voluptate nulla cillum veniam incididunt et id ex. Id aute fugiat esse occaecat dolore eiusmod aliqua et occaecat ut elit velit aute. Pariatur enim nostrud Lorem elit ex dolore est. Officia tempor irure do aliquip cillum ad ex. Nisi commodo occaecat officia cupidatat occaecat ex Lorem duis esse quis do ipsum cupidatat.

Official source

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.

Bayes Estimator, Simulated Annealing and EM

Graph Chatbot

Chat with Graph Search