Progress on modern scientific questions regularly depends on using large-scale datasets to understand complex dynamical systems. An especially challenging case that has grown to prominence with advances in single-cell sequencing technologies is learning the behavior of individuals from population snapshots. In the absence of individual-level time series, standard stochastic differential equation models are often nonidentifiable because intrinsic diffusion cannot be distinguished from measurement noise. Despite the difficulty, accurately recovering diffusion terms is required to answer even basic questions about the system's behavior. We show how to combine population-level time series with velocity measurements to build a provably consistent estimator of the diffusion matrix.
@article{arxiv.2408.14408,
title = {Consistent diffusion matrix estimation from population time series},
author = {Aden Forrow},
journal= {arXiv preprint arXiv:2408.14408},
year = {2024}
}