Exact Estimation of Multiple Directed Acyclic Graphs
This work addresses the challenge of modeling complex dependencies in multi-subject data, such as fMRI, but is incremental as it extends existing ILP methods to multiple DAGs.
The paper tackles the problem of jointly estimating multiple related directed acyclic graph (DAG) models without requiring a common vertex ordering, using an integer linear programming (ILP) approach that allows for unknown dependency structures between DAGs, with results demonstrated on simulated and fMRI data.
This paper considers the problem of estimating the structure of 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, that does not require that the vertices in each DAG share a common ordering. Furthermore, we allow also for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.