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.
|Journal of Physics A: Mathematical and Theoretical
|Udgivet - 13. maj 2022
Bibliografisk noteFunding Information:
S Thapa acknowledges support in the form of a Sackler postdoctoral fellowship and funding from the Pikovsky-Valazzi matching scholarship, Tel Aviv University. J-H Jeon acknowledges support from the National Research Foundation (NRF) of Korea (No. 2020R1A2C4002490). R Metzler acknowledges funding from the German Science Foundation (DFG, Grant No. ME 1525/12-1) and the Foundation for Polish Science (Fundacja na rzecz Nauki Polskiej, FNR) within an Alexander von Humboldt Honorary Polish Research Scholarship.
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