.png){kind=icon}
返回
An Approximate Algorithm for Min-Based Possibilistic Networks
.png){kind=icon}
OA
AI
DOI：10.1002/int.21649.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Min-based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min-based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some observed evidence, in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is known as a hard problem. This paper proposes an approximate algorithm for inference in min-based possibilistic networks. More precisely, we adapt the well-known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP. (C) 2014 Wiley Periodicals, Inc.
Keyword:
PROPAGATION
FUSION
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
引用论文
.png){kind=icon}
Crystal chemistry and metal-hydrogen bonding in anisotropic and interstitial hydrides of intermetallics of rare earth (R) and transition metals (T), RT3 and R2T7稀土 (R) 和过渡金属 (T) 的金属间化合物的各向异性和间隙氢化物中的晶体化学和金属氢键，RT3 和R2T7
PREAI
分享收藏分享收藏
查看更多内容