RT Journal Article
T1 Analyzing the effect of introducing a kurtosis parameter in Gaussian Bayesian networks
A1 Main Yaque, Paloma
A1 Navarro Veguillas, Hilario
AB Gaussian Bayesian networks are graphical models that represent the dependence structure of a multivariate normal random variable with a directed acyclic graph (DAG). In Gaussian Bayesian networks the output is usually the conditional distribution of some unknown variables of interest given a set of evidential nodes whose values are known. The problem of uncertainty about the assumption of normality is very common in applications. Thus a sensitivity analysis of the non-normality effect in our conclusions could be necessary. The aspect of non-normality to be considered is the tail behavior. In this line, the multivariate exponential power distribution is a family depending on a kurtosis parameter that goes from a leptokurtic to a platykurtic distribution with the normal as a mesokurtic distribution. Therefore a more general model can be considered using the multivariate exponential power distribution to describe the joint distribution of a Bayesian network, with a kurtosis parameter reflecting deviations from the normal distribution. The sensitivity of the conclusions to this perturbation is analyzed using the Kullback-Leibler divergence measure that provides an interesting formula to evaluate the effect.
PB Elsevier Sci. Ltd.
SN 0951-8320
YR 2009
FD 2009-05
LK https://hdl.handle.net/20.500.14352/42320
UL https://hdl.handle.net/20.500.14352/42320
LA eng
NO Ministry of Science and Innovation of Spain
NO University Complutense-Community of Madrid
DS Docta Complutense
RD 9 abr 2025