期刊文献+

贝叶斯网络中信息的传递方法 被引量:1

Message Propagation In Bayesian Network
在线阅读 下载PDF
导出
摘要 在简要介绍贝叶斯网络的连接树算法以后以及无证据下的信息的传递方法以后,重点对贝叶斯网络在证据进入的情况下的信息传递进行了重点的介绍,尤其是处理动态信息的情况。 The article briefly introduces the junction tree algorithm and the belief propagation without evidence.It puts its emphasis on the message propagation with evidence,especially when the dynamical evidence enter.
出处 《微计算机信息》 北大核心 2008年第3期124-125,共2页 Control & Automation
基金 国家自然科学基金(90205019)
关键词 连接树 观察值 动态证据 信息传递 Junction Tree Dynamical Evidence Message propagation
  • 相关文献

参考文献8

  • 1[1]Henrik Bengtsson.Bayesian networks[EB/OL].Mathematical tatistics Center for Mathematical Sciences L und Instit ute of Technology,Sweden 2003.
  • 2[2]Kevin Patrick Murphy.Dynamic bayesian networks:representation,inference and learning[EB/oL].A dissertation submitted in partial satisf action of the requirements for the degree of Doctor of Philosophy in Computer Science in the graduate division of the university of California,Berkeley.Fall,2002.
  • 3[3]W,Bacchus F.Learning Bayesian belief networks:An approach based on the MDL principle.Computational Intelligence,1994,10(4):269~293.
  • 4[4]Pearl J.Fusion,propagation,and structuring in belief networks.Artificial Intelligence,Vol.29,No.3,1986,pp241-288
  • 5史建国,高晓光.离散动态贝叶斯网络的直接计算推理算法[J].系统工程与电子技术,2005,27(9):1626-1630. 被引量:36
  • 6王辉.用于决策支持的贝叶斯网络[J].东北师大学报(自然科学版),2001,33(4):26-30. 被引量:18
  • 7刘伟娜,霍利民,张立国.贝叶斯网络精确推理算法的研究[J].微计算机信息,2006,22(03X):92-94. 被引量:33
  • 8[10]C.Huang,A.Darwiche.Inference in belief networks:A procedural guide.Intl.J.Approx.Reasoning,15(3):225-263,1996.

二级参考文献13

  • 1曹锐,李宏光,李昊阳.一类混杂系统Petri网模型的优化算法的研究[J].微计算机信息,2005,21(1):27-28. 被引量:27
  • 2Vladimir Pavlovi'c1, Rehg James M, Tat-Jen Cham. A dynamic bayesian network approach to tracking using learned switching dynamic models[EB]. Compaq Computer Corporation ,2003.
  • 3Vladimir Pavlovi'c, Rehg James M, Tat-Jen Cham, et al. A dynamic bayesian network approach to figure tracking using learned dynamic models[EB]. Compaq Computer Corporation ,2003.
  • 4Henrik Bengtsson. Bayesian networks [ EB/OL ]. Mathematical Statistics Center for Mathematical Sciences Lund Institute of Technology, Sweden 2003.
  • 5Kevin Patrick Murphy. Dynamic bayesian networks: representation, inference and learning[EB/oL]. A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the graduate dision of the university of Cali fornia, Berkeley. Fall, 2002.
  • 6Alexander Kuenzer. An empirical study of Dynamic Bayesian networks for user modeling. Institute of Industrial Engineering and Ergonpmics[EB/OL]. Aachen University of Technology, Germany, 2002.
  • 7Uri Nodelman, Shelton Christian R,Daphne Koller. Learning continuous time bayesian networks [ EB ]. Stanford University.2002.
  • 8Lepar V, Shenoy P P. A comparison of Lauritzen and Spiegelhalter, Hugin and Shafer and Shenoy architectures for computing marginals of probability distributions. Uncertainty in Artificial Intelligence.1998,328-327.
  • 9Andcrs L. Madsen, Finn V. Jensen: Lazy propagation: A junction tree inference algorithm based on lazy evaluation.Artificial Intelligence 113 (1999) 203-245
  • 10Peal J. Fusion, propagation, and structuring in belief networks[J]. Artificial Intelligence 1986.29(3): 241288.

共引文献84

同被引文献4

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部