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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2011-01-01
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doaj-058b34e520fd452a9e9eab456e0eebcb2020-11-25T01:15:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0165e1961610.1371/journal.pone.0019616A Bayesian framework for parameter estimation in dynamical models.Flávio Codeço CoelhoFlávio Codeço CoelhoCláudia Torres CodeçoM Gabriela M GomesMathematical 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.http://europepmc.org/articles/PMC3101204?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Flávio Codeço Coelho Flávio Codeço Coelho Cláudia Torres Codeço M Gabriela M Gomes |
spellingShingle |
Flávio Codeço Coelho Flávio Codeço Coelho Cláudia Torres Codeço M Gabriela M Gomes A Bayesian framework for parameter estimation in dynamical models. PLoS ONE |
author_facet |
Flávio Codeço Coelho Flávio Codeço Coelho Cláudia Torres Codeço M Gabriela M Gomes |
author_sort |
Flávio Codeço Coelho |
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. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
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. |
url |
http://europepmc.org/articles/PMC3101204?pdf=render |
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