TY  - JOUR
A1  - Seckler, Henrik
A1  - Metzler, Ralf
T1  - Bayesian deep learning for error estimation in the analysis of anomalous diffusion
T2  - Nature Communnications
N2  - Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusionmodel and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a wellcalibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
KW  - random-walk
KW  - models
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/58601
SN  - 2041-1723
VL  - 13
PB  - Nature Publishing Group UK
CY  - London
ER  -