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学习贝叶斯网络结构：Dirichlet先验与数据Learning the Bayesian Network Structure: Dirichlet Prior versus Data |
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| 课程网址: | http://videolectures.net/uai08_steck_lbns/ |
| 主讲教师: | Harald Steck |
| 开课单位: | 阿尔卡特-朗讯公司 |
| 开课时间: | 2008-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In the Bayesian approach to structure learning of graphical models, the equivalent sample size (ESS) in the Dirichlet prior over the model parameters was recently shown to have an important effect on the maximum-a-posteriori estimate of the Bayesian network structure. In our first contribution, we theoretically analyze the case of large ESS-values, which complements previous work: among other results, we find that the presence of an edge in a Bayesian network is favored over its absence even if both the Dirichlet prior and the data imply independence, as long as the conditional empirical distribution is notably different from uniform. In our second contribution, we focus on realistic ESS-values, and provide an analytical approximation to the ‘optimal’ ESS-value in a predictive sense (its accuracy is also validated experimentally): this approximation provides an understanding as to which properties of the data have the main effect determining the ‘optimal’ ESS-value. |
| 关 键 词: | 贝叶斯方法; 贝叶斯网络; 狄利克雷 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-15：wuyq |
| 阅读次数: | 90 |