Structural Learning about Directed Acyclic Graphs from Multiple Databases

摘要

We propose an approach for structural learning ofdirected acyclic graphs from multiple databases. We first learn a local structurefrom each database separately, and then we combine these local structurestogether to construct a global graph over all variables. In our approach, wedo not require conditional independence, which is a basic assumption in mostmethods.