A Scoring Criterion For Learning Chain Graphs

作     者：Zhong Guo ZHENG Jing XU Xing Wei TONG 

作者机构：wDepartment of Mathematical Sciences Peking University Beijing 100871 P. R. China Department of Mathematics Beijing Normal University Beijing 100875 P. R. China 

出 版 物：《Acta Mathematica Sinica,English Series》 (数学学报（英文版）)

年 卷 期：2006年第22卷第4期

页      面：1063-1068页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：NNSFC(39930160) BNU Youth Foundation(104951) 

主　　题：Chain graph Markov equivalence Scoring criterion 

摘      要：A chain graph allows both directed and undirected edges, and contains the underlying mathematical properties of the two. An important method of learning graphical models is to use scoring criteria to measure how well the graph structures fit the data. In this paper, we present a scoring criterion for learning chain graphs based on the Kullback Leibler distance. It is score equivalent, that is, equivalent chain graphs obtain the same score, so it can be used to perform model selection and model averaging.