| 贝叶斯网络扩展研究综述 |
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| 引用本文： | 陈英武,高妍方.贝叶斯网络扩展研究综述[J].控制与决策,2008,23(10). |
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| 作者姓名： | 陈英武 高妍方 |
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| 作者单位： | 国防科技大学信息系统与管理学院,长沙,410073 |
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| 基金项目： | 教育部高等学校博士学科点专项科研基金 |
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| 摘 要： | 贝叶斯网络是一种能够对复杂不确定系统进行推理和建模的有效工具,广泛用于不确定决策、数据分析以及智能推理等领域.由于理论和实际的需要,贝叶斯网络不断扩展,出现了各种模型和研究方法.为此,综述了贝叶斯网络在不同领域的扩展模型以及在不同理论框架下的进展,并展望了未来的几个发展方向.
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| 关 键 词： | 贝叶斯网络 扩展模型 核学习 代数 |
| 收稿时间： | 2007-6-21 |
| 修稿时间： | 2007-9-10 |
Survey of extended Bayesian networks |
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| CHEN Ying-wu,GAO Yan-fang.Survey of extended Bayesian networks[J].Control and Decision,2008,23(10). |
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| Authors: | CHEN Ying-wu GAO Yan-fang |
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| Affiliation: | CHEN Ying-wu,GAO Yan-fang(College of Information Systems , Management,National University of Defense Technology,Changsha 410073,China.) |
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| Abstract: | As a modeling and inference tool of complex and uncertainty domain,Bayesian networks has extensive applications in uncertainty decision,data analysis and intelligence inference.It extends in many fields so that some models and research methods emerge with the demand of theory and practice.The extended models in various domains and the development of Bayesian networks with new theory frameworks are reviewed.Finally,some promising areas of future research are briefly discussed. |
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| Keywords: | Bayesian networks Extended model Kernel learning Algebra |
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