.png){kind=icon}
返回
Efficient computation for the noisy MAX
.png){kind=icon}
OA
AI
DOI：10.1002/int.10080.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Diez's algorithm for the noisy MAX is very efficient for polytrees, but when the network has loops, it has to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of the conditional probability of the noisy MAX, are not as efficient for polytrees but can be combined with general propagation algorithms such as clustering or variable elimination, which are more efficient for networks with loops. In this article we propose a new factorization of the noisy MAX that amounts to Diez's algorithm in the case of polytrees and at the same time is more efficient than previous factorizations when combined with either variable elimination or clustering. (C) 2003 Wiley Periodicals, Inc.
Keyword:
INDEPENDENCE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
3.1K
被引数:
8.1K
机构
暂无机构信息
引用论文
.png){kind=icon}
LAZY propagation: A junction tree inference algorithm based on lazy evaluation懒惰传播: 一种基于懒惰评估的联结树推理算法
PREAI
分享收藏
查看更多内容