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.show moreshow less

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Samudrajit Thapa, Michael Andersen Lomholt, Jens KrogORCiD, Andrey G. CherstvyORCiD, Ralf MetzlerORCiD
DOI:https://doi.org/10.1039/c8cp04043e
ISSN:1463-9076
ISSN:1463-9084
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=30255886
Parent Title (English):Physical chemistry, chemical physics : PCCP ; a journal of European Chemical Societies
Publisher:Royal Society of Chemistry
Place of publication:Cambridge
Document Type:Article
Language:English
Year of first Publication:2018
Year of Completion:2018
Release Date:2020/05/18
Volume:20
Issue:46
Pagenumber:20
First Page:29018
Last Page:29037
Funder:Basque Government through the BERC 2014-2017 programmeBasque Government; Basque Government through the BERC 2018-2021 programmeBasque Government; Spanish Ministry of Economy and Competitiveness MINECO through BCAM Polo Programme
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Peer Review:Referiert
Licence (German):License LogoKeine Nutzungslizenz vergeben - es gilt das deutsche Urheberrecht