Bayesian non parametric inference of discrete valued networks

We present a non parametric bayesian inference strategy to automatically infer the number of classes during the clustering pro- cess 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.

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