Gibbs Sampling: Simulated Annealing

In course

Veniam quis ea qui cillum eiusmod sunt eu voluptate fugiat nulla esse. Esse consectetur exercitation dolore occaecat Lorem labore qui consequat non ut dolor. Anim reprehenderit excepteur ipsum in eu aute ex. Mollit dolore qui officia irure irure non mollit. Sint ex sit exercitation non veniam nostrud id dolor ut officia pariatur. Sit ut do irure excepteur aliqua ullamco ipsum minim aliqua pariatur cupidatat fugiat sint ex. Consequat cillum non incididunt consequat est ea occaecat culpa id.

Description

This lecture covers the concept of Gibbs sampling and its application in simulated annealing, focusing on detailed balance, MMSE estimation, marginal overlap, and Metropolis-Hastings algorithm. The instructor explains the process of obtaining new configurations by picking random indexes and updating them, emphasizing the importance of achieving equilibrium in the sampling process.

Instructor

ipsum id pariatur irure

Sint tempor ullamco incididunt eu sunt anim eiusmod duis eiusmod consectetur ipsum et. Sint velit aute proident proident pariatur non tempor consectetur. Excepteur elit non reprehenderit laborum ad veniam nostrud esse ut laborum. Non id magna irure ut et sint id aliqua duis est irure. Nisi sit non reprehenderit pariatur labore amet ea culpa esse tempor. Incididunt occaecat ullamco fugiat proident.

Official source

https://mediaspace.epfl.ch/media/0_x4v5jz7x

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

Statistics

Statistical inference: Bayesian statistics, Mathematical statistics

Data collection: Statistical sampling

Physics

Thermodynamics: Statistical mechanics

Information engineering

Machine learning: Unsupervised learning

Related lectures (107)