Main Yaque, PalomaNavarro Veguillas, Hilario2023-06-202023-06-202009-050951-832010.1016/j.ress.2008.10.004https://hdl.handle.net/20.500.14352/42320Gaussian 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.engjournal articlehttp://www.sciencedirect.com/science/article/pii/S0951832008002548http://www.sciencedirect.com/restricted access517.977Gaussian Bayesian networksKullback-Leibler divergenceExponential power distributionSensitivity analysisEstadística aplicada