EnglishThe expectation-propagation algorithm: a tutorial - Part 1
Multi angle
Auteurs : Barthelmé, Simon (Auteur de la Conférence)
CIRM (Editeur )
62F15 - Bayesian inference
62J12 - Generalized linear models
CIRM (Editeur )
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Résumé :
The Expectation-Propagation algorithm was introduced by Minka in 2001, and is today still one of the most effective algorithms for approximate inference. It is relatively difficult to implement well but in certain cases it can give results that are almost exact, while being much faster than MCMC. In this course I will review EP and classical applications to Generalised Linear Models and Gaussian Process models. I will also introduce some recent developments, including applications of EP to ABC problems, and discuss how to parallelise EP effectively.
Codes MSC : 62F15 - Bayesian inference
62J12 - Generalized linear models
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Informations sur la Vidéo
Langue : AnglaisDate de publication : 16/03/16 Date de captation : 02/03/16 Sous collection : Research talks arXiv category : Methodology ; Computation ; Statistics Theory Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 01:00:20 Audience : Researchers Download : https://videos.cirm-math.fr/2016-03-02_Barthelme.mp4 
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Informations sur la Rencontre
Nom de la rencontre : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmesOrganisateurs de la rencontre : Le Gouic, Thibaut ; Pommeret, Denys ; Willer, Thomas Dates : 29/02/16 - 04/03/16 Année de la rencontre : 2016 URL Congrès : http://conferences.cirm-math.fr/1619.html
Données de citation
DOI : 10.24350/CIRM.V.18937603Citer cette vidéo: Barthelmé, Simon (2016). The expectation-propagation algorithm: a tutorial - Part 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18937603 URI : http://dx.doi.org/10.24350/CIRM.V.18937603 |
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