RT Journal Article
T1 Extreme Inaccuracies In Gaussian Bayesian Networks
A1 Gómez Villegas, Miguel Ángel
A1 Main Yaque, Paloma
A1 Susi García, María Del Rosario
AB 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.
PB Elsevier
SN 0047-259X
YR 2008
FD 2008
LK https://hdl.handle.net/20.500.14352/50038
UL https://hdl.handle.net/20.500.14352/50038
LA eng
NO Gómez Villegas, M. Á., Main Yaque, P. & Susi García, M. R. «Extreme Inaccuracies in Gaussian Bayesian Networks». Journal of Multivariate Analysis, vol. 99, n.o 9, octubre de 2008, pp. 1929-40. DOI.org (Crossref), https://doi.org/10.1016/j.jmva.2008.02.027.
NO Ministerio de Educación, Formación Profesional y Deportes (España)
NO Universidad Complutense de Madrid
NO Comunidad de Madrid
DS Docta Complutense
RD 23 ago 2024