整合逻辑推理和概率链图Integrating Logical Reasoning and Probabilistic Chain Graphs |
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课程网址: | http://videolectures.net/ecmlpkdd09_hommersom_ilrpcg/ |
主讲教师: | Arjen Hommersom |
开课单位: | 内梅亨大学 |
开课时间: | 2009-10-20 |
课程语种: | 英语 |
中文简介: | 在过去几年中,概率逻辑引起了极大的关注。虽然逻辑语言在知识表示和自动推理的研究中占据中心位置,但在涉及不确定性的推理时,具有概率基础的概率图形模型已经占据了类似的位置。链图的形式主义越来越被视为一种自然的概率图形形式,因为它概括了贝叶斯网络和马尔可夫网络,并且具有允许任何贝叶斯网络具有唯一图形表示的语义。同时,链图不支持域的关系方面的建模和学习。在本文中,沿概率Horn逻辑线开发了一种新的概率逻辑链逻辑。该逻辑导致域的关系模型,其中关联和因果知识是相关的,并且可以从数据中学习概率参数。 |
课程简介: | Probabilistic logics have attracted a great deal of attention during the past few years. While logical languages have taken a central position in research on knowledge representation and automated reasoning, probabilistic graphical models with their probabilistic basis have taken up a similar position when it comes to reasoning with uncertainty. The formalism of chain graphs is increasingly seen as a natural probabilistic graphical formalism as it generalises both Bayesian networks and Markov networks, and has a semantics which allows any Bayesian network to have a unique graphical representation. At the same time, chain graphs do not support modelling and learning of relational aspects of a domain. In this paper, a new probabilistic logic, chain logic, is developed along the lines of probabilistic Horn logic. The logic leads to relational models of domains in which associational and causal knowledge are relevant and where probabilistic parameters can be learned from data. |
关 键 词: | 概率逻辑; 不确定性; 贝叶斯网络 |
课程来源: | 视频讲座网 |
最后编审: | 2019-03-24:cwx |
阅读次数: | 131 |