a bayesian framework for parameter estimation in dynamical models一个贝叶斯动态模型中参数估计的框架.pdf

A Bayesian Framework for Parameter Estimation in Dynamical Models ´ 1,2 ´ 3 1 Flavio Codec¸o Coelho *, Claudia Torres Codec¸o , M. Gabriela M. Gomes ˆ ´ ˜ ´ ˜ 1 Instituto Gulbenkian de Ciencia, Oeiras, Portugal, 2 Escola de Matematica Aplicada, Fundac¸ao Getulio Vargas, Rio de Janeiro, Brazil, 3 Programa de Computac¸ao ´ ˜ Cientıfica, Fundac¸ao Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil Abstract 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.