Bayesian inference of scaled versus fractional Brownian motion. (13th May 2022)

• Record Type: Journal Article
• Authors: Thapa, Samudrajit; Park, Seongyu; Kim, Yeongjin; Jeon, Jae-Hyung; Metzler, Ralf; Lomholt, Michael A
• Is Part Of: Journal of physics. Volume 55:Number 19(2022)
• Publication Date: 2022-05-13
• Subjects: Bayesian inference -- scaled Brownian motion -- single particle tracking; Mathematical physics -- Periodicals; Statistical physics -- Periodicals; Quantum theory -- Periodicals; Matter -- Properties -- Periodicals; 530.105
• DOI: 10.1088/1751-8121/ac60e7
• Languages: English
• ISSNs: 1751-8113

Abstract

Abstract: We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a prior different from the data generating one.