Semantics-Preserving Fusion of Structures of Probabilistic Graphical Models

被引：0
|
作者：
Yue, Kun [1 ]
Zhu, Yunlei [1 ]
Tian, Kailin [2 ]
Liu, Weiyi [1 ]
机构：
[1] Yunnan Univ, Dept Comp Sci & Technol, Sch Informat Sci & Engn, Kunming, Peoples R China
[2] Library Southwest Forestry Univ, Kunming, Peoples R China
来源：
FOUNDATIONS OF INTELLIGENT SYSTEMS (ISKE 2011) | 2011年 / 122卷
基金：
中国国家自然科学基金;
关键词：
Probabilistic graphical model (PGM); Bayesian network (BN); Fusion; Conditional independence; Markov equivalence; EQUIVALENCE CLASSES;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper gives an approach for fusing the directed acyclic graphs (DAGs) of BNs, an important and popular probabilistic graphical model (PGM). Considering conditional independence as the semantics implied in a BN, we focus on the DAG fusion while preserving the semantics in all participating BNs. Based on the concept and properties of Markov equivalence, we respectively give the algorithms for fusing equivalent and inequivalent common subgraphs of all participating BNs.
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页码：63 / +
页数：2
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