In this paper, we propose an approach for structural learning of independence graphs from multiple databases or prior knowledge of conditional independencies. In our approach, we first learn a local graph from each database separately, and then we combine these local graphs together to construct a global graph over all variables. This approach can also be used in structural learning to utilize the prior knowledge of conditional independencies. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Zhao, Q., Chen, H., & Geng, Z. (2007). Structural learning about independence graphs from multiple databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4426 LNAI, pp. 1122–1130). Springer Verlag. https://doi.org/10.1007/978-3-540-71701-0_127
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