Bayesian network structure learning by recursive autonomy identification

We propose the recursive autonomy identification (RAI) algorithm for constraint-based 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 substructures. The sequence of operations is performed recursively for each autonomous sub-structure while simultaneously increasing the order of the CI test. In comparison to other constraint-based algorithms d-separating structures and then directing the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. Thereby, learning a structure using the RAI algorithm requires a smaller number of high order CI tests. This reduces the complexity and run-time as well as increases structural and prediction accuracies as demonstrated in extensive experimentation.