Jump to main content
Jump to site search


Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data

Author affiliations

Abstract

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 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.

Graphical abstract: Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data

Back to tab navigation

Publication details

The article was received on 26 Jun 2018, accepted on 10 Sep 2018 and first published on 11 Sep 2018


Article type: Paper
DOI: 10.1039/C8CP04043E
Citation: Phys. Chem. Chem. Phys., 2018, Advance Article
  •   Request permissions

    Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data

    S. Thapa, M. A. Lomholt, J. Krog, A. G. Cherstvy and R. Metzler, Phys. Chem. Chem. Phys., 2018, Advance Article , DOI: 10.1039/C8CP04043E

Search articles by author

Spotlight

Advertisements