LEARNING BAYESIAN NETWORKS FOR REGRESSION FROM INCOMPLETE DATABASES*
In this paper we address the problem of inducing Bayesian network models for regression from incomplete databases. We use mixtures of truncated exponentials (MTEs) to represent the joint distribution in the induced networks. We consider two particular Bayesian network structures, the so-called na¨ıv...
| Hauptverfasser: | , , |
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| Format: | info:eu-repo/semantics/article |
| Sprache: | English |
| Veröffentlicht: |
2017
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| Schlagworte: | |
| Online Zugang: | http://hdl.handle.net/10835/4887 https://doi.org/10.1142/S0218488510006398 |