Exact estimation of multiple directed acyclic graphs

This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not re...

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Bibliographic Details
Published inStatistics and computing Vol. 26; no. 4; pp. 797 - 811
Main Authors Oates, Chris J., Smith, Jim Q., Mukherjee, Sach, Cussens, James
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2016
Subjects
Artificial Intelligence
Mathematics and Statistics
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Multiregression dynamical model
Integer linear programming
Hierarchical model
Joint estimation
Bayesian network
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Summary:This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not require that the vertices in each DAG share a common ordering. Furthermore, we allow for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-015-9570-9