.png){kind=icon}
返回
Learning Bayesian network structure: Towards the essential graph by integer linear programming tools
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2013.09.016.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The basic idea of the geometric approach to learning a Bayesian network (BN) structure is to represent every BN structure by a certain vector. If the vector representative is chosen properly, it allows one to re-formulate the task of finding the global maximum of a score over BN structures as an integer linear programming (ILP) problem. Such a suitable zero-one vector representative is the characteristic imset, introduced by Student, Hemmecke and Lindner in 2010, in the proceedings of the 5th PGM workshop. In this paper, extensions of characteristic imsets are considered which additionally encode chain graphs without flags equivalent to acyclic directed graphs. The main contribution is a polyhedral description of the respective domain of the ILP problem, that is, by means of a set of linear inequalities. This theoretical result opens the way to the application of ILP software packages. The advantage of our approach is that, as a by-product of the ILP optimization procedure, one may get the essential graph, which is a traditional graphical BN representative. We also describe some computational experiments based on this idea. (C) 2013 Elsevier Inc. All rights reserved.
Keyword:
Learning Bayesian network structure
Integer linear programming
Characteristic imset
Essential graph
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
3.0K
被引数:
5.1K
机构
引用论文
Ultrasound‐Guided First Dorsal Compartment Release for Refractory de Quervain Tenosynovitis: A Case Report.png)
PM&R
PM&RIF0
PREAI
分享收藏Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks用于学习贝叶斯网络结构和参数的局部平均贝叶斯Dirichlet度量OAAI
OAAI分享收藏
查看更多内容