贝叶斯网络
贝叶斯概率
计算机科学
人工智能
贝叶斯推理
概率逻辑
机器学习
数据科学
出处
期刊:Ai Magazine
[Association for the Advancement of Artificial Intelligence]
日期:1991-11-01
卷期号:12 (4): 50-63
被引量:753
标识
DOI:10.1609/aimag.v12i4.918
摘要
I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AI-uncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
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