Bayesian model selection with fractional Brownian motion

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift


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.


  • Jens Krog
  • Lars H Jacobsen
  • Frederik W Lund
  • Daniel Wüstner
  • Michael A. Lomholt
Externa organisationer
  • University of Southern Denmark
TidskriftJournal of Statistical Mechanics: Theory and Experiment
StatusPublished - 2018 sep 18
Peer review utfördJa
Externt publiceradJa