图 书 馆
图形模型Graphical Models |
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| 课程网址: | http://videolectures.net/ida07_borgelt_gm/ |
| 主讲教师: | Christian Borgelt |
| 开课单位: | 欧洲软计算中心 |
| 开课时间: | 2007-10-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In the last decade probabilistic graphical models -- in particular Bayes networks and Markov networks -- became very popular as tools for structuring uncertain knowledge about a domain of interest and for building knowledge-based systems that allow sound and efficient inferences about this domain. The lecture gives a brief introduction into the core ideas underlying graphical models, starting from their relational counterparts and highlighting the relation between independence and decomposition. Furthermore, the basics of model construction and evidence propagation are discussed, with an emphasis on join/junction tree propagation. A substantial part of the lecture is then devoted to learning graphical models from data, in which quantitative learning (parameter estimation) as well as the more complex qualitative or structural learning (model selection) are studied. |
| 关 键 词: | 贝叶斯网络; 马尔可夫网络; 参数估计 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-27：cwx |
| 阅读次数: | 65 |