Sensitivity to hyperprior parameters in Gaussian Bayesian networks

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dc.contributor.authorGómez Villegas, Miguel Á.
dc.contributor.authorMain Yaque, Paloma
dc.contributor.authorNavarro, H.
dc.contributor.authorSusi García, María Del Rosario
dc.date.accessioned2023-06-20T09:12:10Z
dc.date.available2023-06-20T09:12:10Z
dc.date.issued2010-06-01
dc.description.abstractOur 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.
dc.description.departmentDepto. de Estadística y Ciencia de los Datos
dc.description.facultyFac. de Estudios Estadísticos
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/10941
dc.identifier.isbn1989-0567
dc.identifier.officialurlhttp://www.ucm.es/info/eue//pagina/cuadernos_trabajo/ct03_2010.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.14352/48928
dc.issue.number03/201
dc.language.isoeng
dc.page.total19
dc.publication.placeMadrid
dc.relation.ispartofseriesCuadernos de Trabajo de la Escuela Universitaria de Estadística
dc.rights.accessRightsopen access
dc.subject.keywordGaussian Bayesian networks
dc.subject.keywordKullback-Leibler divergence
dc.subject.keywordBayesian linear regression
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.ucmInvestigación operativa (Estadística)
dc.subject.unesco1209 Estadística
dc.subject.unesco1207 Investigación Operativa
dc.titleSensitivity to hyperprior parameters in Gaussian Bayesian networks
dc.typetechnical report
dspace.entity.typePublication
relation.isAuthorOfPublicationec909d41-f0c0-40b7-9d6e-1346e1e9ef43
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relation.isAuthorOfPublication.latestForDiscoveryec909d41-f0c0-40b7-9d6e-1346e1e9ef43
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