Bounds on the information divergence for hypergeometric distributions

Peter Harremoës; František Matúš

Kybernetika (2020)

  • Volume: 56, Issue: 6, page 1111-1132
  • ISSN: 0023-5954

Abstract

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The hypergeometric distributions have many important applications, but they have not had sufficient attention in information theory. Hypergeometric distributions can be approximated by binomial distributions or Poisson distributions. In this paper we present upper and lower bounds on information divergence. These bounds are important for statistical testing and for a better understanding of the notion of exchangeability.

How to cite

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Harremoës, Peter, and Matúš, František. "Bounds on the information divergence for hypergeometric distributions." Kybernetika 56.6 (2020): 1111-1132. <http://eudml.org/doc/296966>.

@article{Harremoës2020,
abstract = {The hypergeometric distributions have many important applications, but they have not had sufficient attention in information theory. Hypergeometric distributions can be approximated by binomial distributions or Poisson distributions. In this paper we present upper and lower bounds on information divergence. These bounds are important for statistical testing and for a better understanding of the notion of exchangeability.},
author = {Harremoës, Peter, Matúš, František},
journal = {Kybernetika},
keywords = {binomial distribution; hypergeometric distribution; information divergence; inequalities},
language = {eng},
number = {6},
pages = {1111-1132},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Bounds on the information divergence for hypergeometric distributions},
url = {http://eudml.org/doc/296966},
volume = {56},
year = {2020},
}

TY - JOUR
AU - Harremoës, Peter
AU - Matúš, František
TI - Bounds on the information divergence for hypergeometric distributions
JO - Kybernetika
PY - 2020
PB - Institute of Information Theory and Automation AS CR
VL - 56
IS - 6
SP - 1111
EP - 1132
AB - The hypergeometric distributions have many important applications, but they have not had sufficient attention in information theory. Hypergeometric distributions can be approximated by binomial distributions or Poisson distributions. In this paper we present upper and lower bounds on information divergence. These bounds are important for statistical testing and for a better understanding of the notion of exchangeability.
LA - eng
KW - binomial distribution; hypergeometric distribution; information divergence; inequalities
UR - http://eudml.org/doc/296966
ER -

References

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  5. Harremoës, P., , In: 2014 IEEE International Symposium on Information Theory, IEEE 2014, pp. 2474-2478. DOI
  6. Harremoës, P., Johnson, O., Kontoyiannis, I., Thinning and information projections., arXiv:1601.04255, 2016. MR2807322
  7. Harremoës, P., Ruzankin, P., , IEEE Trans. Inform Theory 50 (2004), 9, 2145-2149. MR2097199DOI
  8. Matúš, F., , In: 2017 IEEE International Symposium on Information Theory (ISIT) 2017, pp. 1451-1454. DOI
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