Inference of Markov models from trajectories via Large Deviations at Level 2.5 with applications to random walks in disordered media

TL;DR

This paper develops a large deviations framework at Level 2.5 to analyze the inference of Markov models from trajectory data, providing explicit rate functions and applications to random walks in disordered media.

Contribution

It introduces a novel large deviations approach at Level 2.5 for Markov model inference, with explicit rate functions for various stochastic processes and applications to disordered media.

Findings

Explicit rate functions for Markov inference in multiple settings

Characterization of fluctuations around true Markov parameters

Applications to random walks in disordered media

Abstract

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.