Structure Learning of Chain Graphs Using the Conditional Independence Tests

WANG Jing-yun; LIU San-yang; ZHU Ming-min

doi:10.3969/j.issn.0372-2112.2017.10.019

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Structure Learning of Chain Graphs Using the Conditional Independence Tests

更新时间：2025-07-16

- Structure Learning of Chain Graphs Using the Conditional Independence Tests
- Acta Electronica Sinica Vol. 45, Issue 10, Pages: 2443-2448(2017)
- 作者机构：
  
  西安电子科技大学数学与统计学院,陕西,西安,710071
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61373174);Youth Fund of National Natural Science Foundation of China (No.11401454)
- DOI：10.3969/j.issn.0372-2112.2017.10.019
  CLC： TP18
- Published：2017
- 稿件说明：
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WANG Jing-yun, LIU San-yang, ZHU Ming-min. Structure Learning of Chain Graphs Using the Conditional Independence Tests[J]. Acta Electronica Sinica, 2017, 45(10): 2443-2448.
DOI：

WANG Jing-yun, LIU San-yang, ZHU Ming-min. Structure Learning of Chain Graphs Using the Conditional Independence Tests[J]. Acta Electronica Sinica, 2017, 45(10): 2443-2448. DOI： 10.3969/j.issn.0372-2112.2017.10.019.

摘要

链图是贝叶斯网络和马尔科夫网络的自然推广，具有较强的表达能力.但目前关于链图结构学习算法的研究较少.本文基于贝叶斯网络结构学习的Grow-Shrink 算法思想，提出一种链图等价类结构学习算法.该算法首先利用网络中结点的局部邻域信息，学习结点的邻接结点恢复网络骨架；然后根据链图复合体有向边的特点，利用条件独立测试确定网络的复合体有向边，从而恢复链图结构.理论分析和实验结果表明了该算法的正确性和有效性.

Abstract

Chain graphs(CGs) including both Bayesian networks(BNs) and Markov networks(MNs) as special cases

can express more independence models compared to the basic probabilistic graphical models.Today there exist several researches on structure learning of chain graphs.In this paper

we propose an algorithm for learning the equivalence classes of a chain graph based on the Grow-Shrink(GS) algorithm for structure learning of Bayesian networks.The algorithm works by first learning the adjacent nodes of each node in a chain graph for recovering its skeleton

and then discovering its complexes using the conditional independence tests and the property of the complexes.Theoretical analysis and experimental results demonstrate the effectiveness and correctness of the proposed algorithm.

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