Structural learning about directed acyclic graphs from multiple databases

DOI10.1155/2012/579543zbMath1256.68089OpenAlexW2116405250WikidataQ58695591 ScholiaQ58695591MaRDI QIDQ1938242

Qiang Zhao

Publication date: 4 February 2013

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/579543

Mathematics Subject Classification ID

Computational learning theory (68Q32) Database theory (68P15) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10)

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Uses Software

• TETRAD

Cites Work

• Causation, prediction, and search
• Probabilities of causation: Bounds and identification
• Ancestral graph Markov models.
• Decomposition of structural learning about directed acyclic graphs
• Mixed Graphical Models with Missing Data and the Partial Imputation EM Algorithm
• On the Desirability of Acyclic Database Schemes
• Embeddings of Homogeneous Spaces in Prime Characteristics
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