Biblioteca de la Universidad Complutense de Madrid

Extreme Inaccuracies In Gaussian Bayesian Networks

Impacto

Gómez Villegas, Miguel A. y Main Yaque, Paloma y Susi García, Rosario (2008) Extreme Inaccuracies In Gaussian Bayesian Networks. Journal Of Multivariate Analysis, 99 (9). pp. 1929-1940. ISSN 0047-259X

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URL Oficial: http://www.sciencedirect.com/science/article/pii/S0047259X08000389



Resumen

To evaluate the impact of model inaccuracies over the network’s output, after the evidence propagation, in a Gaussian Bayesian network, a sensitivity measure is introduced. This sensitivity measure is the Kullback–Leibler divergence and yields different expressions depending on the type of parameter to be perturbed, i.e. on the inaccurate parameter.
In this work, the behavior of this sensitivity measure is studied when model inaccuracies are extreme,i.e. when extreme perturbations of the parameters can exist. Moreover, the sensitivity measure is evaluated for extreme situations of dependence between the main variables of the network and its behavior with extreme inaccuracies. This analysis is performed to find the effect of extreme uncertainty about the initial parameters of the model in a Gaussian Bayesian network and about extreme values of evidence. These ideas and procedures are illustrated with an example.


Tipo de documento:Artículo
Palabras clave:Gaussian Bayesian network; Sensitivity analysis; Kullback-Leibler divergence;Sensitivity-Analysis;Statistics & Probability
Materias:Ciencias > Matemáticas > Estadística matemática
Código ID:15831
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Depositado:05 Jul 2012 09:59
Última Modificación:04 Mar 2016 16:18

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