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Optimal Quantization of the Support of a Continuous Multivariate Distribution based on Mutual Information

Author

Listed:
  • Bernard Colin
  • François Dubeau
  • Hussein Khreibani
  • Jules Tibeiro

Abstract

Based on the notion of mutual information between the components of a random vector, we construct, for data reduction reasons, an optimal quantization of the support of its probability measure. More precisely, we propose a simultaneous discretization of the whole set of the components of the random vector which takes into account, as much as possible, the stochastic dependence between them. Examples are presented. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Bernard Colin & François Dubeau & Hussein Khreibani & Jules Tibeiro, 2013. "Optimal Quantization of the Support of a Continuous Multivariate Distribution based on Mutual Information," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 453-473, October.
  • Handle: RePEc:spr:jclass:v:30:y:2013:i:3:p:453-473
    DOI: 10.1007/s00357-013-9127-6
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    References listed on IDEAS

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    1. Darbellay, Georges A., 1999. "An estimator of the mutual information based on a criterion for conditional independence," Computational Statistics & Data Analysis, Elsevier, vol. 32(1), pages 1-17, November.
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    Cited by:

    1. Alessandro Barbiero & Asmerilda Hitaj, 2022. "Approximation of continuous random variables for the evaluation of the reliability parameter of complex stress–strength models," Annals of Operations Research, Springer, vol. 315(2), pages 1573-1598, August.

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