Inferring Kinetics and Entropy Production from Observable Transitions in Partially Accessible, Periodically Driven Markov Networks

For a network of discrete states with a periodically driven Markovian dynamics, we develop an inference scheme for an external observer who has access to some transitions. Based on waiting-time distributions between these transitions, the periodic probabilities of states connected by these observed...

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Bibliographic Details
Published in:Journal of statistical physics 2024-08, Vol.191 (8), Article 104
Main Authors: Maier, Alexander M., Degünther, Julius, van der Meer, Jann, Seifert, Udo
Format: Article
Language:English
Subjects:
Accessibility
Entropy
Lower bounds
Mathematical and Computational Physics
Physical Chemistry
Physics
Physics and Astronomy
Quantum Physics
Statistical Physics and Dynamical Systems
Theoretical
Time dependence
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Summary:For a network of discrete states with a periodically driven Markovian dynamics, we develop an inference scheme for an external observer who has access to some transitions. Based on waiting-time distributions between these transitions, the periodic probabilities of states connected by these observed transitions and their time-dependent transition rates can be inferred. Moreover, the smallest number of hidden transitions between accessible ones and some of their transition rates can be extracted. We prove and conjecture lower bounds on the total entropy production for such periodic stationary states. Even though our techniques are based on generalizations of known methods for steady states, we obtain original results for those as well.
ISSN:1572-9613
0022-4715
1572-9613
DOI:10.1007/s10955-024-03315-7