期刊文献+

1(1/2)片联合树算法在动态贝叶斯网精确推理中的应用 被引量:3

An Application of 1(1/2) Slice Junction Tree Algorithm to the Exact Inference in DBNs
下载PDF
导出
摘要 基于动态贝叶斯网络处理动态不确定性问题的过程中推理是非常重要的,而推理算法的优劣决定着推理的执行效率。该文提出一种较简单的112片联合树算法,在不需要限制消去顺序且只作一次扩展的条件下构造联合树,所以算法简单且具有较小的复杂度。 To solve the dynamic uncertainty problem which is based on DBNs,the efficiency of process is decided by inference algorithm.The paper presents a new 1 slices Junction Tree algorithm which don't need elimination order limited and constitute the Junction Tree only one times.So it's more simple and has a lower complexity.
出处 《计算机工程与应用》 CSCD 北大核心 2005年第14期81-84,共4页 Computer Engineering and Applications
基金 安徽省自然科学基金项目(编号:03042305)资助
关键词 动态贝叶斯网络(DBNs) 联合树 马尔可夫模型 Dynamic Bayesian Networks(DBNs),junction tree,Markov models
  • 相关文献

参考文献7

  • 1C Huang ed. Inference in belief networks:A procedural guide[J].International journal of approximate reasoning, 1994; 11:1~158. 被引量:1
  • 2Uffe Kjarulff. A Computational Scheme for Dynamic Bayesian Networks[M].Institute for Electronic Systems Department of Mathematics and Computer Science. 被引量:1
  • 3Kevien P Murphy. Inference and Learning in Hybrid Bayesian Networks[R].Report No UCB/CSD-98-990,1998-01. 被引量:1
  • 4D Rose ed. Algorithmic aspects of vertex elimination on graphs[J].SIAM Journ of Comput, 1976;5:146~160. 被引量:1
  • 5U Kjaerulff. dHugin :A Computational System for dynamic time-sliced Bayesian Networks[J].International Journal of Forecasting. 被引量:1
  • 6James D Park ,Adnan Darwiche.A Differential Semantics for Jointree Algorithms.www.cs.ucla.edu/~j d/pubs/diff-jt -full.pdf. 被引量:1
  • 7Kevien P Murphy. A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables.www.berkeley.edu/~murphyk/publ. 被引量:1

同被引文献23

  • 1张润梅,王浩,姚宏亮,方宝富.影响图及其在Robocup中的应用[J].系统仿真学报,2005,17(1):134-137. 被引量:6
  • 2张自立,刘惟一.基于动态贝叶斯网的状态预测[J].云南大学学报(自然科学版),2007,29(1):35-39. 被引量:4
  • 3顾明亮,马勇.基于高斯混合模型的汉语方言辨识系统[J].计算机工程与应用,2007,43(3):204-206. 被引量:9
  • 4Murphy K. Dynamic Bayesian networks: representation, inference and learning[D]. University of California, Berkeley, 2002 被引量:1
  • 5Jensen F V,Jensen F. Optimal junction trees[C]//Proc, of UAl94. 1994:360-366 被引量:1
  • 6Boyen X, Kollcn D. Tractable inference for complex stochastic processes[C]//Proc, of UAI-98. San Francisco: Morgan Kaufmann, 1998: 33-42 被引量:1
  • 7Dechter R. Bucket elimination: A unifying framework for probabilistic inference[C]//Proc, of UAI-96. San Francisco: Morgan Kaufmann, 1996 : 75-104 被引量:1
  • 8Draper D. Clustering without(thinking about) triangulation[C]//Proc, of UAI-95. San Francisco: Morgan Kaufmann, 1995: 125-133 被引量:1
  • 9Kjaerulff U. Reduction of computational complexity in Bayesian networks through removal of weak dependences[C]//Proc, of UAI-94. 1994 : 374-382 被引量:1
  • 10Paskin M A. Thin junction tree filters frontier for simultaneous localization and mapping[C]//Proc. of IJCAI-03. 2003:1157-1164 被引量:1

引证文献3

二级引证文献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