FrançaisThe expectation-propagation algorithm: a tutorial - Part 1
Multi angle
Authors : Barthelmé, Simon (Author of the conference)
CIRM (Publisher )
62F15 - Bayesian inference
62J12 - Generalized linear models
CIRM (Publisher )
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Abstract :
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.
MSC Codes : 62F15 - Bayesian inference
62J12 - Generalized linear models
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Information on the Video
Language : EnglishAvailable date : 16/03/16 Conference Date : 02/03/16 Subseries : Research talks arXiv category : Methodology ; Computation ; Statistics Theory Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:00:20 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2016-03-02_Barthelme.mp4 
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Information on the Event
Event Title : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmesEvent Organizers : Le Gouic, Thibaut ; Pommeret, Denys ; Willer, Thomas Dates : 29/02/16 - 04/03/16 Event Year : 2016 Event URL : http://conferences.cirm-math.fr/1619.html
Citation Data
DOI : 10.24350/CIRM.V.18937603Cite this video as: 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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