PROJECT TITLE :
Simple entropy estimator for small datasets
A pragmatic approach for entropy estimation is presented. It's based on coincidence detection and its application results in a easy algorithm with 2 attractive options: the tactic can be used without any a priori information concerning the source alphabet cardinality (K) and it can provide useful, though biased, estimates even when the number of samples is less than K.
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