Inference of Markov models from trajectories via large deviations at level 2.5 with applications to random walks in disordered media

The inference of Markov models from data on stochastic dynamical trajectories over the large time-window T is revisited via the large deviations at level 2.5 for the time-empirical density and the time-empirical flows. The goal is to obtain the large deviations properties for the probability distribution of the inferred Markov parameters in order to characterize their possible fluctuations around the true Markov parameters for large T. The explicit rate functions are given for several settings, namely discrete-time Markov chains, continuous-time Markov jump processes, and diffusion processes in dimension d. Applications to various models of random walks in disordered media are described, where the goal is to infer the quenched disordered variables defining a given disordered sample.

Automated summary

This paper revisits the inference of Markov models from stochastic dynamical trajectories data using large deviations at level 2.5, focusing on obtaining the large deviations properties for the probability distribution of inferred Markov parameters. The explicit rate functions are provided for different settings such as discrete-time Markov chains, continuous-time Markov jump processes, and diffusion processes in dimension d, with applications to random walks in disordered media described.