Modal posterior clustering motivated by Hopfield’s network

Ruth Fuentes-García, Ramsés H. Mena and Stephen G. Walker

Computational Statistics & Data Analysis, 2019, vol. 137, issue C, 92-100

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.

Keywords: Conditional maximization; Hopfield network; Modal estimation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:137:y:2019:i:c:p:92-100

DOI: 10.1016/j.csda.2019.02.008

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