Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood

DOI10.1007/s10618-010-0178-6zbMath1235.68152OpenAlexW1970687027MaRDI QIDQ408599

José M. Puerta, Juan L. Mateo, José A. Gámez

Publication date: 11 April 2012

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-010-0178-6

Mathematics Subject Classification ID

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

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Uses Software

• TETRAD
• BENEDICT

Cites Work

• The max-min hill-climbing Bayesian network structure learning algorithm
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• Estimating the dimension of a model
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• Learning Bayesian networks: The combination of knowledge and statistical data
• Learning Bayesian networks in the space of structures by estimation of distribution algorithms
• 10.1162/153244303321897717
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
• A hybrid methodology for learning belief networks: BENEDICT
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