Parallel structural learning of Bayesian networks: Iterative divide and conquer algorithm based on structural fusion

Learning Bayesian Networks (BNs) from high-dimensional data is a complex and time-consuming task. Although the literature includes approaches based on horizontal (instances) or vertical (variables) partitioning, none can guarantee the same theoretical properties as the Greedy Equivalence Search (GES...

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Bibliographic Details

Published in:	Knowledge-based systems Vol. 296; p. 111840
Main Authors:	Laborda, Jorge D., Torrijos, Pablo, Puerta, José M., Gámez, José A.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 19.07.2024
Subjects:	Bayesian network fusion/aggregation Bayesian network learning Distributed machine learning High-dimensional problems Bayesian network fusion/aggregation Distributed machine learning Bayesian network learning High-dimensional problems
ISSN:	0950-7051, 1872-7409
Online Access:	Get full text
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