| 连续因果图的推理及参数学习 |
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| 引用本文： | 王洪春. 连续因果图的推理及参数学习[J]. 微电子学与计算机, 2007, 24(1): 18-20,24 |
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| 作者姓名： | 王洪春 |
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| 作者单位： | 华南理工大学,计算机科学与工程学院,广东,广州,510640;重庆师范大学,数学与计算机科学学院,重庆,400047 |
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| 基金项目： | 重庆市高等学校优秀中青年骨干教师计划;重庆师范大学博士专项基金 |
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| 摘 要： | 因果图理论是利用图形化和直接因果强度来表达知识和因果关系的一种基于概率论的推理方法，而对于连续的因果图连接事件的概率密度函数（或可能性密度函数）是连续因果图能进行推理的关键，一般都是假定它们可由领域专家给出。这在实际中很难办到。文章首先给出了连续因果图的推理过程，然后讨论了如何利用已知数据集。在因果图结构已知的条件下利用参数、非参数、半参数化方法估计出连接事件的概率密度函数的途径。
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| 关 键 词： | 因果图 概率密度函数 参数估计 |
| 文章编号： | 1000-7180（2007）01-0018-03 |
| 修稿时间： | 2005-11-23 |
Reasoning and Learning of the Parameters in Continuous Causality Diagram |
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| WANG Hong-chun. Reasoning and Learning of the Parameters in Continuous Causality Diagram[J]. Microelectronics & Computer, 2007, 24(1): 18-20,24 |
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| Authors: | WANG Hong-chun |
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| Affiliation: | 1. Dept. of Computer and Engineering, South China University of Technology, Guangzhou 510640, China;2. Dept. of Mathematics and Computer Science, Chongqing Normal University, Chongqing 400047 |
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| Abstract: | Causality diagram theory is a methodology based on probability theory, which adopted graphical expression of knowledge and direct causal intensity of causality. The probability density function of linkage events is the basis of the inference. But it is difficult to give it by expert In the paper, we discuss the approaches to learn the parameters (probability density function of linkage events) from a set of data, given a fixed network structure, by the parametric, non-parametric and semi- parametric methods. |
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| Keywords: | Causality diagram Probability density function Parametric estimation |
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