Using Mutual Information to Determine Relevance in Bayesian Networks

机译：使用互信息确定贝叶斯网络中的相关性

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The control of Bayesian network (BN) evaluation is important in the development of real-time decision making systems. Techniques which focus attention by considering the relevance of variables in a BN allow more efficient use of computational resources. The statistical concept of mutual information (MI) between two related random variables can be used to measure relevance. We extend this idea to present a new measure of arc weights in a BN, and show how these can be combined to give a measure of the weight of a region of connected nodes. A heuristic path weight of a node or region relative to a specific query is also given. We present results from experiments which show that the MI weights are better than another measure based on the Bhattacharyya distance.

机译：贝叶斯网络（BN）评估的控制在实时决策系统的开发中很重要。通过考虑BN中变量的相关性来集中注意力的技术可以更有效地利用计算资源。两个相关随机变量之间的互信息（MI）的统计概念可用于衡量相关性。我们扩展了这个想法，以提出一种新的BN中弧度权重的度量，并展示了如何将它们组合起来以度量连接节点区域的权重。还给出了相对于特定查询的节点或区域的启发式路径权重。我们提供的实验结果表明，MI权重比基于Bhattacharyya距离的另一种度量要好。

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来源
《PRICAI'98 : Topics in artificial intelligence》|1998年|399-410|共12页
会议地点 Singapore(SG);Singapore(SG)
作者
A. E. Nicholson; N. Jitnah;
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作者单位

School of Computer Science and Software Engineering, Monash University, Clayton, VIC 3168, Australia;

School of Computer Science and Software Engineering, Monash University, Clayton, VIC 3168, Australia;

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原文格式 PDF
正文语种 eng
中图分类自动化系统理论;
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7. Comparative Evaluation of Reverse Engineering Gene Regulatory Networks with Relevance Networks, Graphical Gaussian Models and Bayesian Networks [O] . Adriano V. Werhli A, Marco Grzegorczyk B, Dirk Husmeier A, 2006

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Using Mutual Information to Determine Relevance in Bayesian Networks

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