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The multiset EM algorithm

Author

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  • Huang, Weihong
  • Chen, Yuguo

Abstract

The expectation–maximization (EM) algorithm is widely used in computing the maximum likelihood estimates when the observations can be viewed as incomplete data. However, the convergence rate of the EM algorithm can be slow especially when a large portion of the data is missing. We propose the multiset EM algorithm that can help the convergence of the EM algorithm. The key idea is to augment the system with a multiset of the missing component, and construct an appropriate joint distribution of the augmented complete data. We demonstrate that the multiset EM algorithm can outperform the EM algorithm, especially when EM has difficulties in convergence and the E-step involves Monte Carlo approximation.

Suggested Citation

  • Huang, Weihong & Chen, Yuguo, 2017. "The multiset EM algorithm," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 41-48.
    • Handle: RePEc:eee:stapro:v:126:y:2017:i:c:p:41-48
    DOI: 10.1016/j.spl.2017.02.021
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    References listed on IDEAS

    as
    1. Leman, Scotland C. & Chen, Yuguo & Lavine, Michael, 2009. "The Multiset Sampler," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1029-1041.
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