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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 distributio[...]texto impreso
Gómez Villegas, Miguel A. ; Main Yaque, Paloma ; Sanz San Miguel, Luis ; Navarro Veguillas, Hilario | North Holland | 2004-01In this paper the asymptotic relationship between the classical p-value and the infimum (over all unimodal and symmetric distributions) of the posterior probability in the point null hypothesis testing problem is analyzed. It is shown that the r[...]texto impreso
The problem of modeling Bayesian networks with continuous nodes deals with discrete approximations and conditional linear Gaussian models. In this article we have considered the possibility of using the exponential power family as conditional pr[...]texto impreso
Gómez Villegas, Miguel A. ; Main Yaque, Paloma ; Navarro Veguillas, Hilario ; Susi, Rosario | Pergamon-Elsevier | 2011In this work, we evaluate the sensitivity of Gaussian Bayesian networks to perturbations or uncertainties in the regression coefficients of the network arcs and the conditional distributions of the variables. The Kullback–Leibler divergence mea[...]texto impreso
The asymptotic behavior in the right tail of the hazard rate function is considered to compare probability distributions. Using this tail ordering, the position of the posterior distribution with respect to the prior and the likelihood distribut[...]
