Bezier interpolation improves the inference of dynamical models from data.

ABSTRACT: Many dynamical systems, from quantum many-body systems to evolving populations to financial markets, are described by stochastic processes. Parameters characterizing such processes can often be inferred using information integrated over stochastic paths. However, estimating time-integrated quantities from real data with limited time resolution is challenging. Here, we propose a framework for accurately estimating time-integrated quantities using Bézier interpolation. We applied our approach to two dynamical inference problems: Determining fitness parameters for evolving populations and inferring forces driving Ornstein-Uhlenbeck processes. We found that Bézier interpolation reduces the estimation bias for both dynamical inference problems. This improvement was especially noticeable for dat

SUBMITTER: Shimagaki K 

PROVIDER: S-EPMC10027371 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature