Structural Learning about Directed Acyclic Graphs from Multiple Databases

Abstract EN

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.