Entropy estimation for partially accessible Markov networks based on imperfect observations: Role of finite resolution and finite statistics

TL;DR

This paper evaluates how finite resolution and limited data affect entropy estimation in Markov networks, comparing three estimators and revealing trade-offs in their accuracy under various observational constraints.

Contribution

It introduces a comparative analysis of entropy estimators for imperfect observations, highlighting the impact of resolution and statistics on their performance.

Findings

Resolved transition estimator performs best with perfect data.

Thermodynamic uncertainty relation is more accurate at low affinities.

Higher resolution can slow convergence of measurement statistics.

Abstract

Estimating entropy production from real observation data can be difficult due to finite resolution in both space and time and finite measurement statistics. We characterize the statistical error introduced by finite sample size and compare the performance of three different entropy estimators under these limitations for two different paradigmatic systems, a four-state Markov network and an augmented Michaelis-Menten reaction scheme. We consider the thermodynamic uncertainty relation, a waiting-time based estimator for resolved transitions and a waiting-time based estimator for blurred transitions in imperfect observation scenarios. For perfect measurement statistics and finite temporal resolution, the estimator based on resolved transitions performs best in all considered scenarios. The thermodynamic uncertainty relation gives a better estimate than the estimator based on blurred transitions at low driving affinities, whereas the latter performs better at high driving affinities. Furthermore, we find that a higher temporal and spatial resolution leads to slower convergence of measurement statistics, implying that for short measurement times, a lower resolution may be beneficial. Additionally, we identify a self-averaging effect for the waiting-time based entropy estimators that can reduce their variance for observations with finite statistics.