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Structural Learning about Directed Acyclic Graphs from Multiple Databases

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  • Qiang Zhao

Abstract

We propose an approach for structural learning of directed acyclic graphs from multiple databases. We first learn a local structure from each database separately, and then we combine these local structures together to construct a global graph over all variables. In our approach, we do not require conditional independence, which is a basic assumption in most methods.

Suggested Citation

  • Qiang Zhao, 2012. "Structural Learning about Directed Acyclic Graphs from Multiple Databases," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:579543
    DOI: 10.1155/2012/579543
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    References listed on IDEAS

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    1. Zhi Geng & Kang Wan & Feng Tao, 2000. "Mixed Graphical Models with Missing Data and the Partial Imputation EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(3), pages 433-444, September.
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