LEARNING BAYESIAN NETWORKS FOR REGRESSION FROM INCOMPLETE DATABASES*

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...

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Podrobná bibliografie
Hlavní autoři: Fernández, Antonio, Nielsen, Jens D., Salmerón Cerdán, Antonio
Médium: info:eu-repo/semantics/article
Jazyk:English
Vydáno: 2017
Témata:
Bayesian netwoorks
Regression
Mixtures of truncated exponentials
Missing data
On-line přístup:http://hdl.handle.net/10835/4887
https://doi.org/10.1142/S0218488510006398

Internet

http://hdl.handle.net/10835/4887
https://doi.org/10.1142/S0218488510006398

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