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A pragmatic entropy and differential entropy estimator for small datasets

2014; Volume: 29; Issue: 1 Linguagem: Inglês

10.14209/jcis.2014.8

1980-6612

Jugurta Montalvão, Romis Attux, Daniel G. Silva,

Bayesian Methods and Mixture Models

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).