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Méthodes bayésiennes variationnelles : concepts et applications en neuroimagerie

Christine Keribin 1, 2 
2 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay
Abstract : Bayesian posterior distributions can be numerically intractable, even by the means of Markov Chains Monte Carlo methods. Bayesian variational methods can then be used to compute directly (and fast) a deterministic approximation of these posterior distributions. This paper describes the principle of variational methods and their applications in the Bayesian inference, surveys the main theoretical results and details two examples in the neuroimage field.
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Submitted on : Monday, February 10, 2014 - 10:56:05 AM
Last modification on : Sunday, June 26, 2022 - 12:00:48 PM


  • HAL Id : hal-00944131, version 1


Christine Keribin. Méthodes bayésiennes variationnelles : concepts et applications en neuroimagerie. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2010, 151 (2), pp.107-131. ⟨hal-00944131⟩



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