• DocumentCode
    3112504
  • Title

    Estimation of the entropy on the basis of its polynomial representation

  • Author

    Vinck, Martin ; Battaglia, Francesco P. ; Balakirsky, Vladimir B. ; Vinck, A. J Han ; Pennartz, Cyriel

  • Author_Institution
    Center for Neurosci., Univ. of Amsterdam, Amsterdam, Netherlands
  • fYear
    2012
  • fDate
    1-6 July 2012
  • Firstpage
    1054
  • Lastpage
    1058
  • Abstract
    An algorithm for estimating the entropy, which is based on the representation of the entropy function as the sum of two polynomial terms, called the polynomial approximation function and the remainder, is proposed. We construct an accurate and unbiased estimate of the value of the polynomial approximation function and use the known Bayesian approach to estimate the remainder. The combined estimator essentially reduces the bias of the constructed estimate as compared to the known estimators. Simulation results that confirm the claim are presented.
  • Keywords
    Bayes methods; entropy; neurophysiology; polynomial approximation; Bayesian approach; discrete memoryless source; entropy estimation; entropy function representation; neurophysiology; polynomial approximation function; polynomial representation; remainder estimation; Approximation methods; Bayesian methods; Entropy; Estimation; Polynomials; Probability distribution; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
  • Conference_Location
    Cambridge, MA
  • ISSN
    2157-8095
  • Print_ISBN
    978-1-4673-2580-6
  • Electronic_ISBN
    2157-8095
  • Type

    conf

  • DOI
    10.1109/ISIT.2012.6283012
  • Filename
    6283012