Estimating mixtures of truncated exponentials from data

The MTE (mixture of truncated exponentials) model allows to deal with Bayesian networks containing discrete and continuous variables simultaneously. One of the features of this model is that standard propagation algorithms can be applied. In this paper, we study the problem of estimating these...

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Détails bibliographiques
Auteurs principaux: Moral, Serafín, Rumí, Rafael, Salmerón Cerdán, Antonio
Format: info:eu-repo/semantics/report
Langue:English
Publié: 2012
Accès en ligne:http://hdl.handle.net/10835/1557
Description
Résumé:The MTE (mixture of truncated exponentials) model allows to deal with Bayesian networks containing discrete and continuous variables simultaneously. One of the features of this model is that standard propagation algorithms can be applied. In this paper, we study the problem of estimating these models from data. We propose an iterative algorithm based on least squares approximation. The performance of the algorithm is tested both with artificial and actual data.