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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Бібліографічні деталі
Автори: Moral, Serafín, Rumí, Rafael, Salmerón Cerdán, Antonio
Формат: info:eu-repo/semantics/report
Мова:English
Опубліковано: 2012
Онлайн доступ:http://hdl.handle.net/10835/1557
Опис
Резюме: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.