图 书 馆
顺序和分解的NML模型Sequential and factorized NML models |
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| 课程网址: | http://videolectures.net/icml08_silander_sfn/ |
| 主讲教师: | Tomi Silander |
| 开课单位: | 赫尔辛基学校 |
| 开课时间: | 2008-08-13 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Currently the most popular model selection criterion for learning Bayesian networks is the Bayesian mixture with a conjugate prior. This method has recently been reported to be very sensitive to the choice of prior hyper-parameters. On the other hand, the general model selection criteria, AIC and BIC are derived through asymptotics and their behavior is suboptimal for small sample sizes. In this work we introduce a new effective scoring criterion for learning Bayesian network structures, the factorized normalized maximum likelihood. This score features no tunable parameters thus avoiding the sensitivity problems of Bayesian scores. The new scoring method also suggests a parametrization of the Bayesian network that is based on the conditional normalized maximum likelihood predictive distribution. |
| 关 键 词: | 贝叶斯网络; 共轭先验; 超参数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-15：yumf |
| 阅读次数: | 71 |