所属单位：数学与统计学院
教研室：统计教研室
发表刊物：Computational Statistics & Data Analysis
项目来源：国家自然科学基金项目
关键字：Bayesian network,Complete separator,Conditional independence,Moral edge,Prime block,Structural learning
摘要：In this paper, we consider how to recover the structure of a Bayesian network from a moral graph. We present a more accurate characterization of moral edges, based on which a complete subset (i.e., a separator) contained in the neighbor set of one vertex of the putative moral edge in some prime block of the moral graph can be chosen. This results in a set of separators needing to be searched generally smaller than the sets required by some existing algorithms. A so-called structure-finder algorithm is proposed for structural learning. The complexity analysis of the proposed algorithm is discussed and compared with those for several existing algorithms.Wealso demonstrate how to construct the moral graph locally from, separately, the Markov blanket, domain knowledge and d-separation trees. Simulation studies are used to evaluate the performances of various strategies for structural learning.Wealso analyze a gene expression data set by using the structure-finder algorithm.
第一作者：徐平峰
论文类型：期刊论文
页面范围：1
是否译文：否
发表时间：2011-12-01