Bayesian inference of scaled versus fractional Brownian motion

被引：16

作者：

Thapa, Samudrajit ^{[1
,2
]}

Park, Seongyu ^{[3
]}

Kim, Yeongjin ^{[3
]}

Jeon, Jae-Hyung ^{[3
]}

Metzler, Ralf ^{[4
]}

Lomholt, Michael A. ^{[5
]}

机构：

[1] Tel Aviv Univ, Sackler Ctr Computat Mol & Mat Sci, IL-6997801 Tel Aviv, Israel

[2] Tel Aviv Univ, Sch Mech Engn, IL-6997801 Tel Aviv, Israel

[3] Pohang Univ Sci & Technol POSTECH, Dept Phys, Pohang 37673, South Korea

[4] Univ Potsdam, Inst Phys & Astron, D-14476 Potsdam, Germany

[5] Univ Southern Denmark, Dept Phys Chem & Pharm, PhyLife, Campusvej 55, DK-5230 Odense M, Denmark

来源：

JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL | 2022年 / 55卷 / 19期

基金：

新加坡国家研究基金会;

关键词：

Bayesian inference; scaled Brownian motion; single particle tracking; ANOMALOUS DIFFUSION; SUBDIFFUSION; COEFFICIENTS; MODELS;

D O I：

10.1088/1751-8121/ac60e7

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：21

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