Bayesian Network Structure Learning by Recursive Autonomy Identification
This paper proposes the Recursive Autonomy Identification (RAI) algorithm for Constraint-Based (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of Conditional Independence (CI) tests, edge direction and structure decomposition into autonomous sub-structures. The sequence of operations is performed recursively for each autonomous substructure while simultaneously increasing the order of the CI test. While other CB algorithms d-separate structures and then direct the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. By this means and due to structure decomposition, learning a structure using RAI requires a smaller number of CI tests of high orders.