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

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Variational Bayes methods and algorithms - Part 1

By Christine Keribin

Appears in 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.

Information about the video

Date of recording 02/03/2016
Date of publication 16/03/2016
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.18938003
Cite this video 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

Domain(s)

Statistics Theory

Bibliography

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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
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