Building General Langevin Models from Discrete Datasets

Many living and complex systems exhibit second-order emergent dynamics. Limited experimental access to the configurational degrees of freedom results in data that appear to be generated by a non-Markovian process. This limitation poses a challenge in the quantitative reconstruction of the model from experimental data, even in the simple case of equilibrium Langevin dynamics of Hamiltonian systems. We develop a novel Bayesian inference approach to learn the parameters of such stochastic effective models from discrete finite-length trajectories. We first discuss the failure of naive inference approaches based on the estimation of derivatives through finite differences, regardless of the time resolution and the length of the sampled trajectories. We then derive, adopting higher-order discretization schemes, maximum-likelihood estimators for the model parameters that provide excellent results even with moderately long trajectories. We apply our method to second-order models of collective motion and show that our results also hold in the presence of interactions