It is well known that Bayesian network structure learning from data is an NP-hard problem. Learning a correct skeleton of a DAG is the foundation of dependency analysis algorithms for this problem. Considering the unreliability of the high order condition independence (CI) tests and the aim to improve the efficiency of a dependency analysis algorithm, the key steps are to use less number of CI tests and reduce the sizes of condition sets as many as possible. Based on these analyses and inspired by the algorithm HPC, we present an algorithm, named efficient hybrid parents and child (EHPC), for learning the adjacent neighbors of every variable. We proof the validity of the algorithm. Compared with state-of-the-art algorithms, the experimental results show that EHPC can handle large network and has better accuracy with fewer number of condition independence tests and smaller size of conditioning set.
Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies
