Thermodynamic inference in partially accessible Markov networks: A unifying perspective from transition-based waiting time distributions

TL;DR

This paper develops a unifying framework for inferring thermodynamic quantities from partially observable Markov networks using waiting time distributions, enabling topology inference and fluctuation theorems.

Contribution

It introduces entropy estimators based on waiting times, providing criteria to determine full entropy production or bounds, and infers network topology and hidden cycles.

Findings

Entropy estimator ratios quantify irreversibility.

Criteria to distinguish full entropy recovery from bounds.

Numerical validation of estimators and bounds.

Abstract

The inference of thermodynamic quantities from the description of an only partially accessible physical system is a central challenge in stochastic thermodynamics. A common approach is coarse-graining, which maps the dynamics of such a system to a reduced effective one. While coarse-graining states of the system into compound ones is a well studied concept, recent evidence hints at a complementary description by considering observable transitions and waiting times. In this work, we consider waiting time distributions between two consecutive transitions of a partially observable Markov network. We formulate an entropy estimator using their ratios to quantify irreversibility. Depending on the complexity of the underlying network, we formulate criteria to infer whether the entropy estimator recovers the full physical entropy production or whether it just provides a lower bound that improves on established results. This conceptual approach, which is based on the irreversibility of underlying cycles, additionally enables us to derive estimators for the topology of the network, i.e., the presence of a hidden cycle, its number of states and its driving affinity. Adopting an equivalent semi-Markov description, our results can be condensed into a fluctuation theorem for the corresponding semi-Markov process. This mathematical perspective provides a unifying framework for the entropy estimators considered here and established earlier ones. The crucial role of the correct version of time-reversal helps to clarify a recent debate on the meaning of formal versus physical irreversibility. Extensive numerical calculations based on a direct evaluation of waiting-time distributions illustrate our exact results and provide an estimate on the quality of the bounds for affinities of hidden cycles.