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Fast Bayesian inference of optical trap stiffness and particle diffusion
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 expe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5282562/ https://www.ncbi.nlm.nih.gov/pubmed/28139705 http://dx.doi.org/10.1038/srep41638 |
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author | Bera, Sudipta Paul, Shuvojit Singh, Rajesh Ghosh, Dipanjan Kundu, Avijit Banerjee, Ayan Adhikari, R. |
author_facet | Bera, Sudipta Paul, Shuvojit Singh, Rajesh Ghosh, Dipanjan Kundu, Avijit Banerjee, Ayan Adhikari, R. |
author_sort | Bera, Sudipta |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5282562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-52825622017-02-03 Fast Bayesian inference of optical trap stiffness and particle diffusion Bera, Sudipta Paul, Shuvojit Singh, Rajesh Ghosh, Dipanjan Kundu, Avijit Banerjee, Ayan Adhikari, R. Sci Rep Article 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. Nature Publishing Group 2017-01-31 /pmc/articles/PMC5282562/ /pubmed/28139705 http://dx.doi.org/10.1038/srep41638 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Bera, Sudipta Paul, Shuvojit Singh, Rajesh Ghosh, Dipanjan Kundu, Avijit Banerjee, Ayan Adhikari, R. Fast Bayesian inference of optical trap stiffness and particle diffusion |
title | Fast Bayesian inference of optical trap stiffness and particle diffusion |
title_full | Fast Bayesian inference of optical trap stiffness and particle diffusion |
title_fullStr | Fast Bayesian inference of optical trap stiffness and particle diffusion |
title_full_unstemmed | Fast Bayesian inference of optical trap stiffness and particle diffusion |
title_short | Fast Bayesian inference of optical trap stiffness and particle diffusion |
title_sort | fast bayesian inference of optical trap stiffness and particle diffusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5282562/ https://www.ncbi.nlm.nih.gov/pubmed/28139705 http://dx.doi.org/10.1038/srep41638 |
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