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基于贝叶斯网络不确定推理的研究 被引量：23

On Uncertain Inference Based on the Bayesian Network
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摘要 本文介绍了贝叶斯定理和贝叶斯网络的基本概念 ,提出可以利用贝叶斯网络表示和处理智能信息系统中的不确定性 ,讨论了贝叶斯网络的推理方法 ,并给出一个示范性的例子 。
作者 余东峰 孙兆林
机构地区 并京市 长沙国防科技大学ATR重点实验室
出处 《微型电脑应用》 2004年第8期6-8,共3页 Microcomputer Applications
关键词 人工智能 贝叶斯网络 不确定推理 随机变量 知识表示模型
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献14

  • 1慕春棣,tsinghua.edu.cn,戴剑彬,叶俊.用于数据挖掘的贝叶斯网络[J].软件学报,2000,11(5):660-666. 被引量：97
  • 2林士敏,田凤占,陆玉昌.贝叶斯网络的建造及其在数据采掘中的应用[J].清华大学学报（自然科学版）,2001,41(1):49-52. 被引量：66
  • 3Mulgund Sandep S. Harper K. A. Zacharias G. L.SAMPLE:Situation Awareness Model for Pilot inthe -Loop Evaluation. Proceedings of the 9th Conference on Computer Generated Forces and Behavior Representation, Orlando, FL(May). (2000)
  • 4Gonsalves P. , Rinkus G. , Das S. ″A Hybrid Artificial Intelligence Architecture for Battlefield Information Fusion. ″ Proceedings of the 2nd International Conference on Information Fusion, Sunnyvale, CA(July). 1999
  • 5Mulgund S. , Rinkus, G. , Illgen C. , ＆ Friskie, J.″OLIPSA: On- Line Intelligent Processor for Situation Assessment″. Second Annual Symposium and Exhibiton on Situational Swareness in the Tactical Air Environment, Patuxent River, MD,1997.
  • 6Perarl J. Fusion, propagation,and structuring in belief network. Artificial Intelligence, Vol. 29, No. 3, 1986,pp241-288
  • 7Adam X. Miao,Greg L. Zacharias. A Computational Situation Assessment Model for Nuclear Power Plant Operations, IEEE Trans. on SMC, 27(6),1997. pp728-742
  • 8K. B. Laskey, Hypothesis Management in SituationSpecific Network Construct. 17th Conf. On UAI, San Mateo, CA:Morgan Kanfmann, 2001
  • 9Hongjun Li. An Introduction to Belief Network. Technical Research Report. CSHCN T. R. 99-31
  • 10Greg. L. Zacharals. SAMPLE:Situation Awareness Model for Pilot In=The -Loop valuation. Proceedings of the 1th Annual Symposium on Situational Awareness in the Tactical Air Enviroment, Patuxent River, MD, 1996

二级参考文献21

  • 11.Chickering D. Learning equivalence classes of Bayesian networks structures. In: Horvitz E, Jensen F ed. Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1996. 54～61
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  • 77.Buntine W. Theory refinement on Bayesian networks. In: Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann Publishers, Inc., 1991. 52～61
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  • 99.Russell S, Binder J, Koller D et al. Local learning in probabilistic networks with hidden variables. In: Cooper G F, Moral S ed. Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1998. 1146～1152
  • 101999-03-15

共引文献155

同被引文献153

引证文献23

二级引证文献62

微型电脑应用
2004年 第8期

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