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Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC

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Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC

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Calatayud-Gregori, J.; Cortés, J.; Jornet-Sanz, M.; Villanueva Micó, RJ. (2018). Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC. Mathematical Methods in the Applied Sciences. 41(18):9618-9627. https://doi.org/10.1002/mma.5315

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/125225

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Title: Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC
Author: Calatayud-Gregori, Julia Cortés, J.-C. Jornet-Sanz, Marc Villanueva Micó, Rafael Jacinto
UPV Unit: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Issued date:
Abstract:
[EN] Population dynamics models consisting of nonlinear difference equations allow us to get a better understanding of the processes involved in epidemiology. Usually, these mathematical models are studied under a deterministic ...[+]
Subjects: Adaptive gPC , Computational methods for stochastic equations , Computational uncertainty quantification , Random nonlinear difference equations model , Random population dynamics model , Random time-discrete epidemiological model , Stochastic difference equations
Copyrigths: Reserva de todos los derechos
Source:
Mathematical Methods in the Applied Sciences. (issn: 0170-4214 )
DOI: 10.1002/mma.5315
Publisher:
John Wiley & Sons
Publisher version: http://doi.org/10.1002/mma.5315
Project ID:
AEI/MTM2017-89664-P-AR
Thanks:
This work has been supported by the Spanish Ministerio de Economia y Competitividad grant MTM2017-89664-P. Marc Jornet acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigacion y Desarrollo ...[+]
Type: Artículo

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