Additive noise model structure learning based on rank correlation

DOI10.1016/j.ins.2021.05.061OpenAlexW3172129323MaRDI QIDQ6092080

Gaojin Fan, Aiguo Wang, Kai Xie, Qiqi Chen, Jing Yang

Publication date: 23 November 2023

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.05.061

zbMATH Keywords

causal discoveryrank correlationreal dataarbitrary distributionadditive noise model

Mathematics Subject Classification ID

Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• The max-min hill-climbing Bayesian network structure learning algorithm
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• Learning Bayesian networks: The combination of knowledge and statistical data
• Bayesian network based label correlation analysis for multi-label classifier chain
• Streaming feature-based causal structure learning algorithm with symmetrical uncertainty
• Matching the grade correlation coefficient using a copula with maximum disorder
• TESTS FOR RANK CORRELATION COEFFICIENTS. I
• Significance Testing of the Spearman Rank Correlation Coefficient
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