In this paper, we tackle structure learning of Directed Acyclic Graphs (DAGs), with the idea of exploiting available prior knowledge of the domain at hand to guide the search of the best structure. In particular, we assume to know the topological ordering of variables in addition to the given data. We study a new algorithm for learning the structure of DAGs, proving its theoretical consistence in the limit of infinite observations. Furthermore, we experimentally compare the proposed algorithm to a number of popular competitors, in order to study its behavior in finite samples.

翻译：本文探讨有向无环图的结构学习问题，提出利用领域先验知识引导最优结构搜索的方法。具体而言，我们假设在给定数据基础上已知变量的拓扑排序。本研究提出一种新的有向无环图结构学习算法，并在无限观测极限下证明其理论一致性。此外，我们通过实验将所提算法与若干主流方法进行对比，以研究其在有限样本下的表现特征。