A Bayesian Framework for Parameter Estimation in Dynamical Models
Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real syst...
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| Format: | Online |
| Language: | English |
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Public Library of Science
2011
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101204/ |
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pubmed-31012042011-05-31 A Bayesian Framework for Parameter Estimation in Dynamical Models Coelho, Flávio Codeço Codeço, Cláudia Torres Gomes, M. Gabriela M. Research Article Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal. Public Library of Science 2011-05-24 /pmc/articles/PMC3101204/ /pubmed/21629684 http://dx.doi.org/10.1371/journal.pone.0019616 Text en Coelho et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Coelho, Flávio Codeço Codeço, Cláudia Torres Gomes, M. Gabriela M. |
| spellingShingle |
Coelho, Flávio Codeço Codeço, Cláudia Torres Gomes, M. Gabriela M. A Bayesian Framework for Parameter Estimation in Dynamical Models |
| author_facet |
Coelho, Flávio Codeço Codeço, Cláudia Torres Gomes, M. Gabriela M. |
| author_sort |
Coelho, Flávio Codeço |
| title |
A Bayesian Framework for Parameter Estimation in Dynamical Models |
| title_short |
A Bayesian Framework for Parameter Estimation in Dynamical Models |
| title_full |
A Bayesian Framework for Parameter Estimation in Dynamical Models |
| title_fullStr |
A Bayesian Framework for Parameter Estimation in Dynamical Models |
| title_full_unstemmed |
A Bayesian Framework for Parameter Estimation in Dynamical Models |
| title_sort |
bayesian framework for parameter estimation in dynamical models |
| description |
Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal. |
| publisher |
Public Library of Science |
| publishDate |
2011 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101204/ |
| _version_ |
1611455557179277312 |