RT Journal Article
T1 Sensitivity Analysis in Gaussian Bayesian Networks Using a Divergence Measure
A1 Gómez Villegas, Miguel A.
A1 Main Yaque, Paloma
A1 Susi García, María Del Rosario
AB This article develops a method for computing the sensitivity analysis in a Gaussian Bayesian network. The measure presented is based on the Kullback–Leibler divergence and is useful to evaluate the impact of prior changes over the posterior marginal density of the target variable in the network. We find that some changes do not disturb the posterior marginal density of interest. Finally, we describe a method to compare different sensitivity measures obtained depending on where the inaccuracy was. An example is used to illustrate the concepts and methods presented.
PB Taylor & Francis
SN 1532-415X
YR 2007
FD 2007
LK https://hdl.handle.net/20.500.14352/49802
UL https://hdl.handle.net/20.500.14352/49802
LA eng
NO MEC
NO MTM
NO UCM
DS Docta Complutense
RD 6 may 2024