Exploiting Graphical Structure in Model Search for High Dimensional Data

本研究计划的主要目标是表征子模型关系的等价类的有向无环图中存在的潜在和/或选择变量，并利用学习的结构，在开发一个模型搜索过程。 特别是，我们将提供最大祖先图（MAG）子模型关系的图形标准，并将其扩展到MAG等价类。申请人已经确定了DAG子模型关系的五个图形标准，与先前的公式不同，其依赖于在给定图中保留关于马尔可夫关系的所有信息的配置。第一步是证明这些标准也适用于等价类的有向无环图（DAG），然后将这些结果扩展到更广泛的类的祖先图利用上述配置。 我们将提供一个多项式时间算法来测试两个给定图的标准。 下一步是开发利用子模型关系的模型搜索，并可扩展到高维。 特别是，这些结果将允许第一个搜索和评分程序的结构学习的MAG。

The main objective of this research program is to characterize the submodel relation for equivalence classes of directed acyclic graphs in the presence of latent and/or selection variables and to exploit the learned structures in developing a model search procedure. In particular, we will provide graphical criteria for the maximal ancestral graph (MAG) submodel relation and extend them to MAG equivalence classes. The applicant has determined five graphical criteria for the DAG submodel relation that, unlike previous formulations, rely on configurations that retain all the information about the Markov relations in a given graph. The first step is to prove that these criteria are also adaptable to equivalence classes of directed acyclic graphs (DAGs) and then to extend these results to the broader class of ancestral graphs by exploiting the abovementioned configurations. We will provide a polynomial time algorithm for testing the criteria for two given graphs. The next step is to develop model searches that exploit the submodel relations and are scalable to high dimensions. In particular, these results would permit the first search-and-score procedure for structure learning of MAGs.