Bayesian non parametric inference of discrete valued networks
Résumé
We present a non parametric bayesian inference strategy to automatically infer the number of classes during the clustering process of a discrete valued random network. Our methodology is related to the Dirichlet process mixture models and inference is performed using a Blocked Gibbs sampling procedure. Using simulated data, we show that our approach improves over competitive variational inference clustering methods.
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papier_ESANN2.pdf (106.56 Ko)
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slides.pdf (330.35 Ko)
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Format | Autre |
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