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Register a new userConsistency of the maximum likelihood and variational estimators in a dynamic stochastic block model
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Added by
Lea Longepierre
Publication date
2019
fields
Mathematical Statistics
and research's language is
English
Authors
Lea Longepierre
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We consider a dynamic version of the stochastic block model, in which the nodes are partitioned into latent classes and the connection between two nodes is drawn from a Bernoulli distribution depending on the classes of these two nodes. The temporal evolution is modeled through a hidden Markov chain on the nodes memberships. We prove the consistency (as the number of nodes and time steps increase) of the maximum likelihood and variational estimators of the model parameters, and obtain upper bounds on the rates of convergence of these estimators. We also explore the particular case where the number of time steps is fixed and connectivity parameters are allowed to vary.
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