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Estimating mixtures of truncated exponentials from data
dc.contributor.author | Moral, Serafín | |
dc.contributor.author | Rumí, Rafael | |
dc.contributor.author | Salmerón Cerdán, Antonio | |
dc.date.accessioned | 2012-05-28T09:50:38Z | |
dc.date.available | 2012-05-28T09:50:38Z | |
dc.date.issued | 2002 | |
dc.identifier.citation | Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02), pp. 135-143. | es_ES |
dc.identifier.uri | http://hdl.handle.net/10835/1557 | |
dc.description.abstract | 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. | es_ES |
dc.language.iso | en | es_ES |
dc.source | First European Workshop on Probabilistic Graphical Models (PGM'02) | es_ES |
dc.title | Estimating mixtures of truncated exponentials from data | es_ES |
dc.type | info:eu-repo/semantics/report | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |