标题： 利用部分顺序信息估计高斯有向无环图及其在奶牛数据中的应用 显示英文标题

标题： Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data

摘要： 从观察数据中估计有向无环图（DAG）是一个典型的学习问题，并在近年来引起了广泛关注。 研究主要集中在以下两种情况：当没有关于DAG中节点顺序的信息时，以及当有特定领域的完整节点顺序时。 在本文中，受奶业科学中一个最近应用的启发，我们开发了一种用于中间场景的DAG估计方法，其中基于特定领域的知识已知节点的基于分区的部分顺序。我们开发了一种高效的算法来解决提出的问题，称为Partition-DAG。 通过使用DREAM3酵母数据进行大量模拟，我们说明Partition-DAG能够有效结合部分顺序信息以提高速度和准确性。 然后，我们通过将Partition-DAG应用于最近收集的奶牛数据，并推断奶业农业生态系统中各种变量之间的关系，说明了Partition-DAG的实用性。 显示英文摘要

摘要： Estimating a directed acyclic graph (DAG) from observational data represents a canonical learning problem and has generated a lot of interest in recent years. Research has focused mostly on the following two cases: when no information regarding the ordering of the nodes in the DAG is available, and when a domain-specific complete ordering of the nodes is available. In this paper, motivated by a recent application in dairy science, we develop a method for DAG estimation for the middle scenario, where partition based partial ordering of the nodes is known based on domain specific knowledge.We develop an efficient algorithm that solves the posited problem, coined Partition-DAG. Through extensive simulations using the DREAM3 Yeast data, we illustrate that Partition-DAG effectively incorporates the partial ordering information to improve both speed and accuracy. We then illustrate the usefulness of Partition-DAG by applying it to recently collected dairy cattle data, and inferring relationships between various variables involved in dairy agroecosystems.