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Operationally accessible uncertainty relations for thermodynamically consistent semi-Markov processes

Benjamin Ertel, Jann van der Meer, Udo Seifert

2022Physical review. E24 citationsDOIOpen Access PDF

Abstract

Semi-Markov processes generalize Markov processes by adding temporal memory effects as expressed by a semi-Markov kernel. We recall the path weight for a semi-Markov trajectory and the fact that thermodynamic consistency in equilibrium imposes a crucial condition called direction-time independence for which we present an alternative derivation. We prove a thermodynamic uncertainty relation that formally resembles the one for a discrete-time Markov process. The result relates the entropy production of the semi-Markov process to mean and variance of steady-state currents. We prove a further thermodynamic uncertainty relation valid for semi-Markov descriptions of coarse-grained Markov processes that emerge by grouping states together. A violation of this inequality can be used as an inference tool to conclude that a given semi-Markov process cannot result from coarse graining an underlying Markov one. We illustrate these results with representative examples.

Topics & Concepts

MathematicsMarkov processMarkov chainConsistency (knowledge bases)Entropy (arrow of time)Markov propertyThermodynamic processRelation (database)Statistical physicsGranularityMarkov kernelVariable-order Markov modelMarkov renewal processInferenceIndependence (probability theory)Entropy productionMarkov modelTrajectoryProcess (computing)Computer scienceWeak consistencyStochastic processMathematical economicsPath (computing)Gene Regulatory Network AnalysisAdvanced Thermodynamics and Statistical MechanicsNeural dynamics and brain function