Estimating the Mutual Information between Two Discrete, Asymmetric Variables with Limited Samples
Estimating the Mutual Information between Two Discrete, Asymmetric Variables with Limited Samples is a scholarly work by Inés Samengo, published in 2019 in ''Entropy''. The main subjects of the publication include statistical physics, computer science, mutual information, econophysics, artificial neural network, statistics, and mathematics. The authors obtain a consistent estimator that presents very low bias, outperforming previous methods even when the sampled data contain few coincidences.