严格定性影响的贝叶斯网络的参数学习][Parameter Learning for Bayesian Networks with Strict Qualitative Influences]

严格定性影响的贝叶斯网络的参数学习 Parameter Learning for Bayesian Networks with Strict Qualitative Influences


课程网址:	http://videolectures.net/ida07_feelders_plfbn/
主讲教师:	Ad Feelders
开课单位:	荷兰乌得勒支大学
开课时间:	2007-10-08
课程语种:	英语
中文简介:	提出了一种新的定性影响贝叶斯网络参数学习方法。该方法的目的是消除由序约束最大似然估计（OCML）产生的不需要的（上下文特定的）独立性。这是通过用一阶逻辑回归模型的拟合概率平均OCML估计量来实现的。实验结果表明，新的学习算法的性能不比OCML差，并且解决了大部分的独立性问题。
课程简介:	We propose a new method for learning the parameters of a Bayesian network with qualitative influences. The proposed method aims to remove unwanted (context-specific) independencies that are created by the order-constrained maximum likelihood (OCML) estimator. This is achieved by averaging the OCML estimator with the fitted probabilities of a first-order logistic regression model. We show experimentally that the new learning algorithm does not perform worse than OCML, and resolves a large part of the independencies.
关键词:	贝叶斯网络; 最大似然估计; 平均信号估计; 拟合概率
课程来源:	视频讲座网
最后编审:	2019-12-20：lxf
阅读次数:	63

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