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

Abstract

We employ Bayesian statistics and nested-sampling algorithm to compare and rank multiple models of ergodic diffusion (including anomalous diffusion models) as well as to assess their optimal parameters for dedicated in silico generated 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 evaluate relative probabilities and best parameter sets for each diffusion model, comparing the parameters to the true ones. We test the performance of the 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 data record. We present model-ranking results in application to experimental data of tracer diffusion in polymeric mucin hydrogels.

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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, Accepted Manuscript
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    Bayesian nested sampling analysis of single particle tracking data: maximum likelihood model selection applied to stochastic diffusivity data

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

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