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Triplet Markov Chains Based- Estimation of Nonstationary Latent Variables Hidden with Independent Noise

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Enterprise Information Systems (ICEIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 321))

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Abstract

Estimation of hidden variables is among the most challenging tasks in statistical signal processing. In this context, hidden Markov chains have been extensively used due to their ability to recover hidden variables from observed ones even for large data. Such models fail, however, to handle nonstationary data when parameters are unknown. The aim of this paper is to show how the recent triplet Markov chains, strictly more general models exhibiting comparable computational cost, can be used to overcome this shortcoming in two different ways: (i) in a firmly Bayesian context by considering an additional Markov process to model the switches of the hidden variables; and, (ii) by introducing Dempster-Shafer theory to model the lack of precision of prior distributions. Moreover, we analyze both approaches and assess their performance through experiments conducted on sampled data and noised images.

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Author information

Authors and Affiliations

  1. Ecole Militaire Polytechnique, PO Box 17, 16111, Bordj El Bahri, Algiers, Algeria

    Mohamed El Yazid Boudaren, Kadda Beghdad Bey & Ahmed Habbouchi

  2. TELECOM SudParis, CNRS, Université Paris-Saclay, 9 rue Charles Fourier, 91000, Evry, France

    Emmanuel Monfrini & Wojciech Pieczynski

Authors
  1. Mohamed El Yazid Boudaren
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  2. Emmanuel Monfrini
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  3. Kadda Beghdad Bey
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  4. Ahmed Habbouchi
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  5. Wojciech Pieczynski
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Correspondence to Mohamed El Yazid Boudaren .

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Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. MODESTE/ESEO, Angers, France

    Olivier Camp

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Poland

    Joaquim Filipe

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Boudaren, M.E.Y., Monfrini, E., Beghdad Bey, K., Habbouchi, A., Pieczynski, W. (2018). Triplet Markov Chains Based- Estimation of Nonstationary Latent Variables Hidden with Independent Noise. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-93375-7_7

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