Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data

Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called -learning algorithm, for learning moral graphs for high-dimensional Bayesian networks...

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Vydané v:Neural computation Ročník 31; číslo 6; s. 1183
Hlavní autori: Xu, Suwa, Jia, Bochao, Liang, Faming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.06.2019
Predmet:
Algorithms
Bayes Theorem
Humans
Neural Networks, Computer
ISSN:1530-888X, 1530-888X
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Shrnutí:Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called -learning algorithm, for learning moral graphs for high-dimensional Bayesian networks. The moral graph is a Markov network representation of the Bayesian network and also the key to construction of the Bayesian network for constraint-based algorithms. The consistency of the -learning algorithm is justified under the small- , large- scenario. The numerical results indicate that the -learning algorithm significantly outperforms the existing ones, such as the PC, grow-shrink, incremental association, semi-interleaved hiton, hill-climbing, and max-min hill-climbing. Under the sparsity assumption, the -learning algorithm has a computational complexity of even in the worst case, while the existing algorithms have a computational complexity of in the worst case.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1530-888X
1530-888X
DOI:10.1162/neco_a_01190