Convergence of the groups posterior distribution in latent or stochastic block models. (English) Zbl 1329.62285

The paper deals with latent block models and, particularly, with stochastic block models in order to facilitate the clustering analysis. The authors focus on a procedure able to cluster both objects and variables (i.e., to simultaneously cluster rows and columns of a data matrix). They propose a unified framework to establish sufficient conditions for the convergence of the groups posterior distribution to a Dirac mass located at the actual random groups configuration.


62H30 Classification and discrimination; cluster analysis (statistical aspects)


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