Maximum likelihood estimations of force and mobility from short single Brownian trajectories

TL;DR

This paper introduces a maximum likelihood-based method to estimate force and mobility parameters from short, possibly out-of-equilibrium Brownian trajectories, effective even with limited data and spatially varying parameters.

Contribution

It presents a novel approach for analyzing single Brownian trajectories to extract dynamic parameters, applicable to out-of-equilibrium conditions and limited data scenarios.

Findings

Effective for short trajectories

Works with out-of-equilibrium data

Validated with experimental and simulated data

Abstract

We describe a method to extract force and diffusion parameters from single trajectories of Brownian particles based on the principle of maximum likelihood. The analysis is well-suited for out-of-equilibrium trajectories, even when a limited amount of data is available and the dynamical parameters vary spatially. We substantiate this method with experimental and simulated data, and discuss its practical implementation, strengths, and limitations.