Modal posterior clustering motivated by Hopfield's network

Abstract Motivated by the Hopfield’s network, a conditional maximization routine is used in order to compute the posterior mode of a random allocation model. The proposed approach applies to a general framework covering parametric and nonparametric Bayesian mixture models, product partition models, and change point models, among others. The resulting algorithm is simple to code and very fast, thus providing a highly competitive alternative to Markov chain Monte Carlo methods. Illustrations with both simulated and real data sets are presented.

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