Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.
Biostatistics
; 17(4): 619-33, 2016 10.
Article
en En
| MEDLINE
| ID: mdl-26993062
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings.
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Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Distribución Normal
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Procesos Estocásticos
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Teorema de Bayes
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Epidemias
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Modelos Teóricos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Biostatistics
Año:
2016
Tipo del documento:
Article