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 )
Loading the player...
|
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
|
Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de Publication : 16/03/16 Date de Captation : 02/03/16 Sous Collection : Research talks Catégorie arXiv : Statistics Theory Domaine(s) : Probabilités & Statistiques Format : MP4 (.mp4) - HD Audience : Chercheurs Download : https://videos.cirm-math.fr/2016-03-02_Keribin.mp4 
|
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 de la Rencontre : 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
- Bishop, C.M. (2006). Pattern recognition and machine learning. New York : Springer - http://www.springer.com/us/book/9780387310732
- Consonni, G., Marin, J.-M. (2007). Mean-field variational approximate Bayesian inference for latent variable models. Computational Statistics & Data Analysis, 52(2), 790-798 - http://dx.doi.org/10.1016/j.csda.2006.10.028
- Govaert, G., & Nadif, M. (2008) Block clustering with Bernoulli mixture models: Comparison of different approaches. Computational Statistics & Data Analysis, 52(6), 3233-3245 - http://dx.doi.org/10.1016/j.csda.2007.09.007
- Hall, P.,Humphreys, K., & Titterington, M. (2002). On the adequacy of variational lower bound functions for likelihood-based inference in Markovian models with missing values. Journal of the Royal Statistical Society: Series B, 64(3), 549-564 - http://dx.doi.org/10.1111/1467-9868.00350
- Keribin, C., Brault, V., Celeux, G., & Govaert, G. (2014). Estimation and selection for the latent block model on categorical data. Statistics and Computing, vol. 25(6), 1201-1216 - http://dx.doi.org/10.1007/s11222-014-9472-2
- 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
- Murphy, K.P. (2012). Machine learning. A probabilistic perspective. Cambridge : MIT Press - https://mitpress.mit.edu/books/machine-learning-0
- Wang, B., & Titterington, M. (2006). Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model. Bayesian Analysis, 1(3), 625-650 - http://projecteuclid.org/euclid.ba/1340371055
- 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
- Woolrich, M.W., & Behrens, T. E. (2006). Variational Bayes inference for spatial mixture models for segmentation. IEEE Transactions on Medical Imaging, 25(10), 1380-1391 - http://dx.doi.org/10.1109/tmi.2006.880682
- 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
