Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data
- We employ Bayesian statistics using the nested-sampling algorithm to compare and rank multiple models of ergodic diffusion (including anomalous diffusion) as well as to assess their optimal parameters for in silico-generated and real time-series. We focus on the recently-introduced model of Brownian motion with "diffusing diffusivity'-giving rise to widely-observed non-Gaussian displacement statistics-and its comparison to Brownian and fractional Brownian motion, also for the time-series with some measurement noise. We conduct this model-assessment analysis using Bayesian statistics and the nested-sampling algorithm on the level of individual particle trajectories. We evaluate relative model probabilities and compute best-parameter sets for each diffusion model, comparing the estimated parameters to the true ones. We test the performance of the nested-sampling algorithm and its predictive power both for computer-generated (idealised) trajectories as well as for real single-particle-tracking trajectories. Our approach delivers newWe employ Bayesian statistics using the nested-sampling algorithm to compare and rank multiple models of ergodic diffusion (including anomalous diffusion) as well as to assess their optimal parameters for in silico-generated and real time-series. We focus on the recently-introduced model of Brownian motion with "diffusing diffusivity'-giving rise to widely-observed non-Gaussian displacement statistics-and its comparison to Brownian and fractional Brownian motion, also for the time-series with some measurement noise. We conduct this model-assessment analysis using Bayesian statistics and the nested-sampling algorithm on the level of individual particle trajectories. We evaluate relative model probabilities and compute best-parameter sets for each diffusion model, comparing the estimated parameters to the true ones. We test the performance of the nested-sampling algorithm and its predictive power both for computer-generated (idealised) trajectories as well as for real single-particle-tracking trajectories. Our approach delivers new important insight into the objective selection of the most suitable stochastic model for a given time-series. We also present first model-ranking results in application to experimental data of tracer diffusion in polymer-based hydrogels.…
Author details: | Samudrajit ThapaORCiDGND, Michael Andersen LomholtORCiD, Jens KrogORCiD, Andrey G. CherstvyORCiDGND, Ralf MetzlerORCiDGND |
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DOI: | https://doi.org/10.1039/c8cp04043e |
ISSN: | 1463-9076 |
ISSN: | 1463-9084 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/30255886 |
Title of parent work (English): | Physical chemistry, chemical physics : PCCP ; a journal of European Chemical Societies |
Publisher: | Royal Society of Chemistry |
Place of publishing: | Cambridge |
Publication type: | Article |
Language: | English |
Year of first publication: | 2018 |
Publication year: | 2018 |
Release date: | 2020/05/18 |
Volume: | 20 |
Issue: | 46 |
Number of pages: | 20 |
First page: | 29018 |
Last Page: | 29037 |
Funding institution: | Det Frie Forskningsrad (DFF), Danish Natural Science Research Council, German Research Foundation (DFG), Humboldt Polish Honorary Research Scholarship (Foundation for Polish Science), Deutscher Akademischer Austauschdienst (DAAD) |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
Peer review: | Referiert |
License (German): | Keine öffentliche Lizenz: Unter Urheberrechtsschutz |