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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| Vydáno v: | Neural computation Ročník 31; číslo 6; s. 1183 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
01.06.2019
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| Témata: | |
| ISSN: | 1530-888X, 1530-888X |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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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. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1530-888X 1530-888X |
| DOI: | 10.1162/neco_a_01190 |