Inferring kinetics and entropy production from observable transitions in partially accessible, periodically driven Markov networks

Abstract

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.

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• Compensating random transition-detection blackouts in Markov networks
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• A pedestrian's approach to large deviations in semi-Markov processes with an application to entropy production
• Coarse Graining Photo-Isomerization Reactions: Thermodynamic Consistency and Implications for Molecular Ratchets
• Counting observables in stochastic excursions