Bayesian inference of scaled versus fractional Brownian motion
- 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.
| Author details: | Samudrajit ThapaORCiDGND, Seongyu ParkORCiD, Yeongjin KimORCiD, Jae-Hyung JeonORCiD, Ralf MetzlerORCiDGND, Michael A. LomholtORCiD |
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| DOI: | https://doi.org/10.1088/1751-8121/ac60e7 |
| ISSN: | 1751-8113 |
| ISSN: | 1751-8121 |
| Title of parent work (English): | Journal of physics : A, mathematical and theoretical |
| Publisher: | IOP Publ. Ltd. |
| Place of publishing: | Bristol |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2022/04/12 |
| Publication year: | 2022 |
| Release date: | 2024/04/05 |
| Tag: | Bayesian inference; scaled Brownian motion; single particle tracking |
| Volume: | 55 |
| Issue: | 19 |
| Article number: | 194003 |
| Number of pages: | 21 |
| Funding institution: | Sackler postdoctoral fellowship; Pikovsky-Valazzi matching scholarship; Tel Aviv University; National Research Foundation (NRF) of Korea; [2020R1A2C4002490]; German Science Foundation (DFG) [ME 1525/12-1];; Foundation for Polish Science (Fundacja na rzecz Nauki Polskiej, FNR); within an Alexander von Humboldt Honorary Polish Research Scholarship |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
| Peer review: | Referiert |
