  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.
