Fast Bayesian inference of optical trap stiffness and particle diffusion
Autor: | Rameshwar Adhikari, Sudipta K. Bera, Dipanjan Ghosh, Ayan Banerjee, Rajesh Singh, Shuvojit Paul, Avijit Kundu |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
Multidisciplinary
Stochastic process Computer science Bayesian probability Monte Carlo method FOS: Physical sciences Probability and statistics 02 engineering and technology Condensed Matter - Soft Condensed Matter 021001 nanoscience & nanotechnology Bayesian inference 01 natural sciences Article Physics - Data Analysis Statistics and Probability 0103 physical sciences Maximum a posteriori estimation Soft Condensed Matter (cond-mat.soft) Statistical physics Diffusion (business) 010306 general physics 0210 nano-technology Data Analysis Statistics and Probability (physics.data-an) Brownian motion |
Zdroj: | Scientific Reports |
ISSN: | 2045-2322 |
DOI: | 10.1038/srep41638 |
Popis: | Bayesian inference provides a principled way of estimating the parameters of a stochastic process that is observed discretely in time. The overdamped Brownian motion of a particle confined in an optical trap is generally modelled by the Ornstein-Uhlenbeck process and can be observed directly in experiment. Here we present Bayesian methods for inferring the parameters of this process, the trap stiffness and the particle diffusion coefficient, that use exact likelihoods and sufficient statistics to arrive at simple expressions for the maximum a posteriori estimates. This obviates the need for Monte Carlo sampling and yields methods that are both fast and accurate. We apply these to experimental data and demonstrate their advantage over commonly used non-Bayesian fitting methods. minor changes and added journal references |
Databáze: | OpenAIRE |
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