English
Variational Bayes methods and algorithms - Part 1
Multi angle
Auteurs : Keribin, Christine (Auteur de la Conférence)
CIRM (Editeur )
49J40 - Variational methods including variational inequalities
62F15 - Bayesian inference
62H12 - Multivariate estimation
CIRM (Editeur )
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Résumé :
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.
Codes MSC : 49J40 - Variational methods including variational inequalities
62F15 - Bayesian inference
62H12 - Multivariate estimation
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 16/03/16 Date de captation : 02/03/16 Sous collection : Research talks arXiv category : Statistics Theory Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Audience : Researchers Download : https://videos.cirm-math.fr/2016-03-02_Keribin.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.18938003Citer cette vidéo: 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 |
Voir aussi
- [Multi angle] The expectation-propagation algorithm: a tutorial - Part 1 / Auteur de la Conférence Barthelmé, Simon.
- [Multi angle] Exploring the presence of complex dependence structures in epidemiological and genomic data through flexible clustering / Auteur de la Conférence Richardson, Sylvia.
- [Multi angle] Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1 / Auteur de la Conférence Marin, Jean-Michel.
- [ Post-edited] Markov Chain Monte Carlo Methods - Part 1 / Auteur de la Conférence Robert, Christian P..
Bibliographie
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- Keribin, C. (2010). Méthodes bayésiennes variationnelles : concepts et applications en neuroimagerie. Journal de la Société Française de Statistique, 151(2), 107-131 - http://journal-sfds.fr/index.php/J-SFdS/article/view/51/42
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- Wang, B., & Titterington, M. (2004). Lack of consistency of mean field and variational Bayes approximations for state space models. Neural Processing Letters, 20(3), 151-170 - http://dx.doi.org/10.1007/s11063-004-2024-6
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- Wyse, J., & Friel, N. (2012). Block clustering with collapsed latent block models. Statistics and Computing, 22(2), 415-428 - http://dx.doi.org/10.1007/s11222-011-9233-4
