Bayesian inference of scaled versus fractional Brownian motion

Samudrajit Thapa, Seongyu Park, Yeongjin Kim, Jae Hyung Jeon, Ralf Metzler, Michael A. Lomholt*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

42 Downloads (Pure)


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.

Original languageEnglish
Article number194003
JournalJournal of Physics A: Mathematical and Theoretical
Issue number19
Publication statusPublished - 13. May 2022


  • Bayesian inference
  • scaled Brownian motion
  • single particle tracking


Dive into the research topics of 'Bayesian inference of scaled versus fractional Brownian motion'. Together they form a unique fingerprint.

Cite this