Main Article Content
A pragmatic approach for entropy estimation is presented, first for discrete variables, then in the form of an extension for handling continuous and/or multivariate ones. It is based on coincidence detection, and its application leads to algorithms with three main attractive features: they are easy to use, can be employed without any a priori knowledge concerning source distribution (not even the alphabet cardinality K of discrete sources) and can provide useful estimates even when the number of samples is small (e.g. less than K, for discrete variables).
How to Cite
Montalvão, J., Attux, R., & Silva, D. (2014). A pragmatic entropy and differential entropy estimator for small datasets. Journal of Communication and Information Systems, 29(1). https://doi.org/10.14209/jcis.2014.8
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