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...
| Auteurs principaux: | , , |
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| Format: | info:eu-repo/semantics/report |
| Langue: | English |
| Publié: |
2012
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| Accès en ligne: | http://hdl.handle.net/10835/1557 |
| 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. |
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