On Generalized Schürmann Entropy Estimators
Copyright policy )On Generalized Schürmann Entropy Estimators
We present a new class of estimators of Shannon entropy for severely undersampled discrete distributions. It is based on a generalization of an estimator proposed by T. Schürmann, which itself is a generalization of an estimator proposed by myself.For a special set of parameters, they are completely free of bias and have a finite variance, something which is widely believed to be impossible. We present also detailed numerical tests, where we compare them with other recent estimators and with exact results, and point out a clash with Bayesian estimators for mutual information.
FOS: Computer and information sciences, bias, Science, QC1-999, Computer Science - Information Theory, entropy estimates, mutual information estimates, FOS: Physical sciences, variance, Astrophysics, Bayesian, info:eu-repo/classification/ddc/510, Condensed Matter - Statistical Mechanics, Statistical Mechanics (cond-mat.stat-mech), Physics, Brief Report, Information Theory (cs.IT), Q, undersampling, QB460-466, Physics - Data Analysis, Statistics and Probability, Data Analysis, Statistics and Probability (physics.data-an)
FOS: Computer and information sciences, bias, Science, QC1-999, Computer Science - Information Theory, entropy estimates, mutual information estimates, FOS: Physical sciences, variance, Astrophysics, Bayesian, info:eu-repo/classification/ddc/510, Condensed Matter - Statistical Mechanics, Statistical Mechanics (cond-mat.stat-mech), Physics, Brief Report, Information Theory (cs.IT), Q, undersampling, QB460-466, Physics - Data Analysis, Statistics and Probability, Data Analysis, Statistics and Probability (physics.data-an)
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