Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.
J R Soc Interface
; 6(31): 187-202, 2009 Feb 06.
Article
en En
| MEDLINE
| ID: mdl-19205079
ABSTRACT
Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Teorema de Bayes
/
Modelos Biológicos
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J R Soc Interface
Año:
2009
Tipo del documento:
Article
País de afiliación:
Reino Unido