论文标题
指导结构学习DAG的计数数据
Guided structure learning of DAGs for count data
论文作者
论文摘要
在本文中，我们解决了定向无环图（DAG）的结构学习，并提出了利用可用的当前域的可用知识来指导最佳结构的搜索。特别是，我们假设除了给定数据外，还知道变量的拓扑排序。我们研究了一种用于学习DAG结构的新算法，证明了其在无限观察限制中的理论一致性。此外，我们通过实验性地将所提出的算法与许多流行竞争者进行比较，以研究其在有限样本中的行为。
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.
