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...

תיאור מלא

מידע ביבליוגרפי
Main Authors: Fernández, Antonio, Nielsen, Jens D., Salmerón Cerdán, Antonio
פורמט: info:eu-repo/semantics/article
שפה:English
יצא לאור: 2017
נושאים:
Bayesian netwoorks
Regression
Mixtures of truncated exponentials
Missing data
גישה מקוונת:http://hdl.handle.net/10835/4887
https://doi.org/10.1142/S0218488510006398

אינטרנט

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

פריטים דומים