Biblioteca de la Universidad Complutense de Madrid

Sensitivity to hyperprior parameters in Gaussian Bayesian networks

Impacto



Gómez Villegas, Miguel Á. y Main Yaque, Paloma y Navarro, H. y Susi García, Rosario (2010) Sensitivity to hyperprior parameters in Gaussian Bayesian networks. [ Cuadernos de Trabajo de la Escuela Universitaria de Estadística; nº 03/201, ]

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URL Oficial: http://www.ucm.es/info/eue//pagina/cuadernos_trabajo/ct03_2010.pdf



Resumen

Our focus is on learning Gaussian Bayesian networks (GBNs) from data. In GBNs the multivariate normal joint distribution can be alternatively specified by the normal regression models of each variable given its parents in the DAG (directed acyclic graph). In the later representation the paramenters are the mean vector, the regression coefficients and the corresponding conditional variances. the problem of Bayesian learning in this context has been handled with different approximations, all of them concerning the use of different priors for the parameters considered we work with the most usual prior given by the normal/inverse gamma form. In this setting we are inteserested in evaluating the effect of prior hyperparameters choice on posterior distribution. The Kullback-Leibler divergence measure is used as a tool to define local sensitivity comparing the prior and posterior deviations. This method can be useful to decide the values to be chosen for the hyperparameters.


Tipo de documento:Documento de trabajo o Informe técnico
Palabras clave:Gaussian Bayesian networks, Kullback-Leibler divergence, Bayesian linear regression
Materias:Ciencias > Matemáticas > Estadística matemática
Ciencias > Estadística > Investigación Operativa
Título de serie o colección:Cuadernos de Trabajo de la Escuela Universitaria de Estadística
Volumen:
Número:03/201
Código ID:10941
Depositado:30 Jun 2010 10:26
Última Modificación:14 Mar 2016 11:31

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