首页 | 本学科首页   官方微博 | 高级检索  
     检索      

多值动态不确定因果图的推理算法研究
引用本文:郑海,王洪春.多值动态不确定因果图的推理算法研究[J].重庆师范大学学报(自然科学版),2023,40(4):107-116.
作者姓名:郑海  王洪春
作者单位:重庆师范大学 数学科学学院, 重庆 401331
基金项目:国家社会科学基金一般项目(No.13BTJ008);重庆市教育委员会人文社会科学重点项目(No.22SKGH081);重庆市教育委员会重庆市高等教育教学改革研究项目(No.213139)
摘    要:利用领域知识求解多值动态不确定因果图的联合概率分布所涉及的领域因果图的概率分布表达式构造难度大,针对这一问题,从2类因果循环图出发,提出一种基于图分解的推理算法。该算法极大地简化了全局概率分布表达式的构造过程,有效地降低了领域因果图概率分布表达式构造的难度。提高了多值动态不确定因果图的推理效率。

关 键 词:多值动态不确定因果图  有向循环图  推理算法  参数学习  概率推理

Research on Inference Algorithm of Multivalued Dynamic Uncertain Causality Graph
ZHENG Hai,WANG Hongchun.Research on Inference Algorithm of Multivalued Dynamic Uncertain Causality Graph[J].Journal of Chongqing Normal University:Natural Science Edition,2023,40(4):107-116.
Authors:ZHENG Hai  WANG Hongchun
Institution:School of Mathematical Science, Chongqing Normal University, Chongqing 401331, China
Abstract:In order to solve the joint probability distribution of Multivalued Dynamic Uncertain Causality Graph with domain knowledge, it is difficult to construct the probability distribution expression of domain causal graphs. An inference algorithm based on graph decomposition is proposed from two kinds of cyclic causal graphs, which greatly simplifies the construction of global probability distribution expression, effectively reduces the difficulty of constructing probability distribution expression of domain causal graphs, and improves the inference efficiency of multi-valued dynamic uncertain causal graphs.
Keywords:multivalued dynamic uncertain causality graph  directed cyclic graph  reasoning algorithm  parameter learning  probabilistic reasoning
点击此处可从《重庆师范大学学报(自然科学版)》浏览原始摘要信息
点击此处可从《重庆师范大学学报(自然科学版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号