RT Journal Article
T1 The effect of block parameter perturbations in Gaussian Bayesian networks: Sensitivity and robustness
A1 Gómez Villegas, Miguel Ángel
A1 Main Yaque, Paloma
A1 Susi García, María Del Rosario
AB n this work we study the effects of model inaccuracies on the description of a Gaussian Bayesian network with a set of variables of interest and a set of evidential variables. Using the Kullback-Leibler divergence measure, we compare the output of two different networks after evidence propagation: the original network, and a network with perturbations representing uncertainties in the quantitative parameters. We describe two methods for analyzing the sensitivity and robustness of a Gaussian Bayesian network on this basis. In the sensitivity analysis, different expressions are obtained depending on which set of parameters is considered inaccurate. This fact makes it possible to determine the set of parameters that most strongly disturbs the network output. If all of the divergences are small, we can conclude that the network output is insensitive to the proposed perturbations. The robustness analysis is similar, but considers all potential uncertainties jointly. It thus yields only one divergence, which can be used to confirm the overall sensitivity of the network. Some practical examples of this method are provided, including a complex, real-world problem
PB Elsevier  Science Inc
SN 0020-0255
YR 2013
FD 2013-02
LK https://hdl.handle.net/20.500.14352/33271
UL https://hdl.handle.net/20.500.14352/33271
LA eng
NO Gómez Villegas, M. A., Main Yaque, P. & Susi García, M. R. «The Effect of Block Parameter Perturbations in Gaussian Bayesian Networks: Sensitivity and Robustness». Information Sciences, vol. 222, febrero de 2013, pp. 439-58. DOI.org (Crossref), https://doi.org/10.1016/j.ins.2012.08.004.
NO Ministerio de Ciencia, Innovación y Universidades (España)
NO Metodos Bayesianos by the BSCH-UCM from Spain
DS Docta Complutense
RD 17 abr 2025