Bayesian non parametric inference of discrete valued networksReport as inadecuate

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1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire SAmos-Marin Mersenne

Abstract : 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.

Author: Laetitia Nouedoui - Pierre Latouche -



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