Learning hybrid Bayesian networks using mixtures of truncated exponentials

Learning hybrid Bayesian networks using mixtures of truncated exponentials

In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The struct...

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Bibliographic Details
Main Authors: Romero, Vanessa, Rumí, Rafael, Salmerón Cerdán, Antonio
Format: info:eu-repo/semantics/article
Language:English
Published: 2017
Subjects:
Bayesian networks
Mixtures of truncated exponentials
Continuous variables
Parameter learning
Kernel Methods
Simulated
Online Access:http://hdl.handle.net/10835/4898
https://doi.org/10.1016/j.ijar.2005.10.004

Internet

http://hdl.handle.net/10835/4898
https://doi.org/10.1016/j.ijar.2005.10.004

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