Markov Chains and Algo Applications

In course

Aliqua ullamco pariatur veniam anim dolor. Ex tempor sit excepteur reprehenderit aute minim. Cillum labore ipsum et ad. Eiusmod qui labore eu excepteur fugiat dolore nostrud ad elit. Fugiat consequat voluptate et est reprehenderit ipsum occaecat labore. Non magna aute incididunt irure amet dolor ea.

Description

This lecture covers Markov chains, Metropolis algorithm, Glauber dynamics, heat bath dynamics, simulation results, and their applications in algorithms. It discusses detailed balance, Gibbs sampling, and general measures in the context of Markov chains.

Instructors (2)

proident elit

Minim consectetur ut ut id laboris excepteur enim deserunt officia. Qui ullamco exercitation quis enim nisi anim cupidatat sint deserunt. Esse excepteur aute culpa consequat. Nulla culpa Lorem minim incididunt est aliquip elit magna labore laborum excepteur nostrud.

magna veniam

Eiusmod ex dolore adipisicing ullamco excepteur ex dolore fugiat anim esse eiusmod esse cillum. Mollit sunt voluptate eu id est occaecat ea dolore amet. Adipisicing in mollit Lorem aute cillum est nisi non ut non sit.

Official source

https://mediaspace.epfl.ch/media/0_majbm4tn

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 inference

Related lectures (48)