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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Vydané v:	Knowledge-based systems Ročník 296; s. 111840
Hlavní autori:	Laborda, Jorge D., Torrijos, Pablo, Puerta, José M., Gámez, José A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 19.07.2024
Predmet:	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
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