TY  - JOUR
A1  - Thapa, Samudrajit
A1  - Lomholt, Michael Andersen
A1  - Krog, Jens
A1  - Cherstvy, Andrey G.
A1  - Metzler, Ralf
T1  - Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data
JF  - Physical chemistry, chemical physics : PCCP ; a journal of European Chemical Societies
N2  - 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.
Y1  - 2018
U6  - https://doi.org/10.1039/c8cp04043e
SN  - 1463-9076
SN  - 1463-9084
VL  - 20
IS  - 46
SP  - 29018
EP  - 29037
PB  - Royal Society of Chemistry
CY  - Cambridge
ER  -