Modeling and Unsupervised Classification of Multivariate Hidden Markov Chains With Copulas

@article{Brunel2010ModelingAU,
  title={Modeling and Unsupervised Classification of Multivariate Hidden Markov Chains With Copulas},
  author={N. Brunel and J. Lapuyade-Lahorgue and W. Pieczynski},
  journal={IEEE Transactions on Automatic Control},
  year={2010},
  volume={55},
  pages={338-349}
}
  • N. Brunel, J. Lapuyade-Lahorgue, W. Pieczynski
  • Published 2010
  • Computer Science, Mathematics
  • IEEE Transactions on Automatic Control
  • Parametric modeling and estimation of non-Gaussian multidimensional probability density function is a difficult problem whose solution is required by many applications in signal and image processing. A lot of efforts have been devoted to escape the usual Gaussian assumption by developing perturbed Gaussian models such as spherically invariant random vectors (SIRVs). In this work, we introduce an alternative solution based on copulas that enables theoretically to represent any multivariate… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 65 REFERENCES
    An Introduction to Copulas
    • 2,900
    • Highly Influential
    • PDF
    E
    • 124,245
    • PDF
    A Tutorial on Hidden Markov Models and Selected Applications
    • 23,457
    • PDF
    Adaptive Filter Theory
    • 16,031
    • PDF
    The EM algorithm and extensions
    • 5,723
    • Highly Influential
    • PDF
    Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
    • 716
    • PDF
    Robust $M$-Estimators of Multivariate Location and Scatter
    • 829
    • Highly Influential
    Non-Gaussian random vector identification using spherically invariant random processes
    • 272
    • Highly Influential
    Maximum likelihood estimation via the ECM algorithm: A general framework
    • 1,520
    • PDF