Accuracy of variational estimates for random graph mixture models

Steven S. Gazal (1) , Jean-Jacques J.-J. Daudin (1) , Stephane S. Robin (1)

1 MIA-Paris - Mathématiques et Informatique Appliquées

Variational and variational Bayes techniques are popular approaches for statistical inference of complex models but their theoretical properties are still not well known. Because of both unobserved variables and intricate dependency structures, mixture models for random graphs constitute a good case study. We first present four different variational estimates for the parameters of these models. We then compare their accuracy through simulation studies and show that the variational Bayes estimates seem the most accurate for moderate graph size. We finally re-analyse the regulatory network of Escherichia coli with this approach.

Mots clés: variational Bayes inference; variational inference; stochastic block model; mixture model; random graph model

Domaines: Sciences agricoles

Steven S. Gazal, Jean-Jacques J.-J. Daudin, Stephane S. Robin. Accuracy of variational estimates for random graph mixture models. Journal of Statistical Computation and Simulation, 2012, 82 (6), pp.849-862. ⟨10.1080/00949655.2011.560117⟩. ⟨hal-01000025⟩