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Randomizing the parameters of a Markov chain to model the stroke disease: A technical generalization of established computational methodologies towards improving real applications

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Randomizing the parameters of a Markov chain to model the stroke disease: A technical generalization of established computational methodologies towards improving real applications

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Cortés, J.; Navarro-Quiles, A.; Romero, J.; Roselló, M. (2017). Randomizing the parameters of a Markov chain to model the stroke disease: A technical generalization of established computational methodologies towards improving real applications. Journal of Computational and Applied Mathematics. 324:225-240. doi:10.1016/j.cam.2017.04.040

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/105820

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Title: Randomizing the parameters of a Markov chain to model the stroke disease: A technical generalization of established computational methodologies towards improving real applications
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Issued date:
Abstract:
[EN] Classical Markov models are defined through a stochastic transition matrix, i.e., a matrix whose columns (or rows) are deterministic values representing transition probabilities. However, in practice these quantities ...[+]
Subjects: Markov process , Disease modelling , Random variable transformation technique , Computing the first probability density function
Copyrigths: Embargado
Source:
Journal of Computational and Applied Mathematics. (issn: 0377-0427 )
DOI: 10.1016/j.cam.2017.04.040
Publisher:
Elsevier
Publisher version: http://doi.org/10.1016/j.cam.2017.04.040
Thanks:
This work has been partially supported by the Ministerio de Economia y Competitividad grant MTM2013-41765-P. Ana Navarro-Quiles acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigacion y ...[+]
Type: Artículo

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