Variational Bayes methods and algorithms - Part 1 | Vidéo | Carmin.tv

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Variational Bayes methods and algorithms - Part 1

De Christine Keribin

Apparaît dans la collection : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmes

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.

Informations sur la vidéo

Date de captation 02/03/2016
Date de publication 16/03/2016
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.18938003
Citer cette vidéo Keribin, Christine (02/03/2016). Variational Bayes methods and algorithms - Part 1. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.18938003
URL https://dx.doi.org/10.24350/CIRM.V.18938003

Domaine(s)

Statistiques - Théorie

Bibliographie

Bishop, C.M. (2006). Pattern recognition and machine learning. New York : Springer - http://www.springer.com/us/book/9780387310732
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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
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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
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

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