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The expectation-propagation algorithm: a tutorial - Part 1

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Authors : Barthelmé, Simon (Author of the conference)
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

    Information on the Video

    Film maker : Hennenfent, Guillaume
    Language : English
    Available 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

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 algorithmes
Event 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.18937603
Cite 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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