Low probability states, data statistics, and entropy estimation

TL;DR

This paper investigates how entropy estimators for complex systems rely on data statistics, especially in undersampled scenarios, and derives analytical estimators to improve understanding and estimation of entropy.

Contribution

It reveals the statistical features influencing Bayesian entropy estimators and introduces approximate analytical estimators based on these features.

Findings

Entropy estimators depend mainly on sample size, MLE, coincidences, and their dispersion.

Derived analytical estimators for undersampled distributions.

Provided an intuitive understanding of Bayesian entropy estimators.

Abstract

A fundamental problem in analysis of complex systems is getting a reliable estimate of entropy of their probability distributions over the state space. This is difficult because unsampled states can contribute substantially to the entropy, while they do not contribute to the Maximum Likelihood estimator of entropy, which replaces probabilities by the observed frequencies. Bayesian estimators overcome this obstacle by introducing a model of the low-probability tail of the probability distribution. Which statistical features of the observed data determine the model of the tail, and hence the output of such estimators, remains unclear. Here we show that well-known entropy estimators for probability distributions on discrete state spaces model the structure of the low probability tail based largely on few statistics of the data: the sample size, the Maximum Likelihood estimate, the number of coincidences among the samples, the dispersion of the coincidences. We derive approximate analytical entropy estimators for undersampled distributions based on these statistics, and we use the results to propose an intuitive understanding of how the Bayesian entropy estimators work.