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
Link To Document :
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