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Modal posterior clustering motivated by Hopfield’s network

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

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.

Suggested Citation

  • Fuentes-García, Ruth & Mena, Ramsés H. & Walker, Stephen G., 2019. "Modal posterior clustering motivated by Hopfield’s network," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 92-100.
    • Handle: RePEc:eee:csdana:v:137:y:2019:i:c:p:92-100
    DOI: 10.1016/j.csda.2019.02.008
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    References listed on IDEAS

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    1. Loschi, R.H. & Cruz, F.R.B., 2005. "Extension to the product partition model: computing the probability of a change," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 255-268, February.
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    4. Ruth Fuentes–García & Ramsés Mena & Stephen Walker, 2010. "A Probability for Classification Based on the Dirichlet Process Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 389-403, November.
    5. Fernando A. Quintana & Pilar L. Iglesias, 2003. "Bayesian clustering and product partition models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 557-574, May.
    6. Wang, Xue & Walker, Stephen G., 2017. "An optimal data ordering scheme for Dirichlet process mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 42-52.
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    Cited by:

    1. Burghardt, Elliot & Sewell, Daniel & Cavanaugh, Joseph, 2022. "Agglomerative and divisive hierarchical Bayesian clustering," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

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