Main Yaque, Paloma and Navarro Veguillas, Hilario
(2009)
*Analyzing the effect of introducing a kurtosis parameter in Gaussian Bayesian networks.*
Reliability engineering & systems safety , 94
(5).
pp. 922-926.
ISSN 0951-8320

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Restricted to Repository staff only until 31 December 2020. 521kB |

Official URL: http://www.sciencedirect.com/science/article/pii/S0951832008002548

## Abstract

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.

Item Type: | Article |
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Uncontrolled Keywords: | Gaussian Bayesian networks; Kullback-Leibler divergence; Exponential power distribution; Sensitivity analysis; |

Subjects: | Sciences > Mathematics > Applied statistics |

ID Code: | 16482 |

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Deposited On: | 21 Sep 2012 08:20 |

Last Modified: | 07 Feb 2014 09:30 |

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