Markov trajectories : Microcanonical Ensembles based on empirical observables as compared to Canonical Ensembles based on Markov generators

preprint2022arXivOpen access

Abstract

The Ensemble of trajectories x(0≤t≤T)x(0 \leq t \leq T) produced by the Markov generator MM can be considered as 'Canonical' for the following reasons : (C1) the probability of the trajectory x(0≤t≤T)x(0 \leq t \leq T) can be rewritten as the exponential of a linear combination of its relevant empirical time-averaged observables EnE_n, where the coefficients involving the Markov generator are their fixed conjugate parameters; (C2) the large deviations properties of these empirical observables EnE_n for large TT are governed by the explicit rate function IM[2.5](E.)I^{[2.5]}_M (E_.) at Level 2.5, while in the thermodynamic limit T=+∞T=+\infty, they concentrate on their typical values Entyp[M]E_n^{typ[M]} determined by the Markov generator MM. This concentration property in the thermodynamic limit T=+∞T=+\infty suggests to introduce the notion of the 'Microcanonical Ensemble' at Level 2.5 for stochastic trajectories x(0≤t≤T)x(0 \leq t \leq T), where all the relevant empirical variables EnE_n are fixed to some values En∗E^*_n and cannot fluctuate anymore for finite TT. The goal of the present paper is to discuss its main properties : (MC1) when the long trajectory x(0≤t≤T)x(0 \leq t \leq T) belongs the Microcanonical Ensemble with the fixed empirical observables En∗E_n^*, the statistics of its subtrajectory x(0≤t≤τ)x(0 \leq t \leq τ) for 1≪τ≪T1 \ll τ\ll T is governed by the Canonical Ensemble associated to the Markov generator M∗M^* that would make the empirical observables En∗E_n^* typical ; (MC2) in the Microcanonical Ensemble, the central role is played by the number ΩT[2.5](E.∗)Ω^{[2.5]}_T(E^*_.) of stochastic trajectories of duration TT with the given empirical observables En∗E^*_n, and by the corresponding explicit Boltzmann entropy S[2.5](E.∗)=[ln⁡ΩT[2.5](E.∗)]/TS^{[2.5]}( E^*_. ) = [\ln Ω^{[2.5]}_T(E^*_.)]/T . This general framework is applied to continuous-time Markov Jump processes and to discrete-time Markov chains with illustrative examples.

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