TY  - JOUR
A1  - Thapa, Samudrajit
A1  - Park, Seongyu
A1  - Kim, Yeongjin
A1  - Jeon, Jae-Hyung
A1  - Metzler, Ralf
A1  - Lomholt, Michael A.
T1  - Bayesian inference of scaled versus fractional Brownian motion
JF  - Journal of physics : A, mathematical and theoretical
N2  - 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.
KW  - Bayesian inference
KW  - scaled Brownian motion
KW  - single particle tracking
Y1  - 2022
U6  - https://doi.org/10.1088/1751-8121/ac60e7
SN  - 1751-8113
SN  - 1751-8121
VL  - 55
IS  - 19
PB  - IOP Publ. Ltd.
CY  - Bristol
ER  -