Communication: A multiscale Bayesian inference approach to analyzing subdiffusion in particle trajectories

Abstract : Anomalous diffusion is characterized by its asymptotic behavior for t → ∞. This makes it difficult to detect and describe in particle trajectories from experiments or computer simulations, which are necessarily of finite length. We propose a new approach using Bayesian inference applied directly to the observed trajectories sampled at different time scales. We illustrate the performance of this approach using random trajectories with known statistical properties and then use it for analyzing the motion of lipid molecules in the plane of a lipid bilayer.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-02154066
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Submitted on : Wednesday, June 12, 2019 - 4:31:36 PM
Last modification on : Tuesday, June 18, 2019 - 11:46:04 AM

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Konrad Hinsen, Gérald Kneller. Communication: A multiscale Bayesian inference approach to analyzing subdiffusion in particle trajectories. Journal of Chemical Physics, American Institute of Physics, 2016, 145 (15), pp.151101. ⟨10.1063/1.4965881⟩. ⟨hal-02154066⟩

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