Bayesian model selection with fractional Brownian motion

Jens F. C. Krog, Lars Hervig Jacobsen, Frederik Wendelboe Lund, Daniel Wüstner, Michael Andersen Lomholt

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Abstrakt

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.
OriginalsprogEngelsk
Artikelnummer093501
TidsskriftJournal of Statistical Mechanics: Theory and Experiment
Vol/bind2018
Udgave nummer9
Antal sider23
ISSN1742-5468
DOI
StatusUdgivet - 18. sep. 2018

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