Bayesian model selection with fractional Brownian motion
Data Analysis, Statistics and Probability
2018-04-05 v1 Statistical Mechanics
Abstract
We implement Bayesian model selection and parameter estimation for the case of fractional Brownian motion with measurement noise and a constant drift. The approach is tested on artificial trajectories and shown to make estimates that match well with the underlying true parameters, while for model selection the approach has a preference for simple models when the trajectories are finite. The approach is applied to observed trajectories of vesicles diffusing in Chinese hamster ovary cells. Here it is supplemented with a goodness-of-fit test, which is able to reveal statistical discrepancies between the observed trajectories and model predictions.
Cite
@article{arxiv.1804.01365,
title = {Bayesian model selection with fractional Brownian motion},
author = {Jens Krog and Lars H. Jacobsen and Frederik W. Lund and Daniel Wüstner and Michael A. Lomholt},
journal= {arXiv preprint arXiv:1804.01365},
year = {2018}
}
Comments
10 pages, 5 figures