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Variational Bayes methods and algorithms - Part 1

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Authors : Keribin, Christine (Author of the conference)
CIRM (Publisher )

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Abstract : Bayesian posterior distributions can be numerically intractable, even by the means of Markov Chain Monte Carlo methods. Bayesian variational methods can then be used to compute directly (and fast) a deterministic approximation of these posterior distributions. In this course, I describe the principles of the variational methods and their application in Bayesian inference, review main theoretical results and discuss their use on examples.

MSC Codes :
49J40 - Variational methods including variational inequalities
62F15 - Bayesian inference
62H12 - Multivariate estimation

    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 : Statistics Theory
    Mathematical Area(s) : Probability & Statistics
    Format : MP4 (.mp4) - HD
    Targeted Audience : Researchers
    Download : https://videos.cirm-math.fr/2016-03-02_Keribin.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.18938003
Cite this video as: Keribin, Christine (2016). Variational Bayes methods and algorithms - Part 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18938003
URI : http://dx.doi.org/10.24350/CIRM.V.18938003

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