Bayesian non parametric inference of discrete valued networks

Laetitia Nouedoui (1) , Pierre Latouche (1)

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

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.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Laetitia Nouedoui, Pierre Latouche. Bayesian non parametric inference of discrete valued networks. 21-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), 2013, Bruges, Belgium. pp.291-296. ⟨hal-00825966⟩