Structural Learning about Directed Acyclic Graphs from Multiple Databases | Zendy

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Structural Learning about Directed Acyclic Graphs from Multiple Databases

Author(s) -

Qiang Zhao

Publication year - 2012

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2012/579543

Subject(s) - directed acyclic graph , conditional independence , construct (python library) , mathematics , directed graph , independence (probability theory) , graph , theoretical computer science , database , computer science , artificial intelligence , algorithm , programming language , statistics

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

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