图 书 馆
动态非参数混合模型与经常性中餐回流过程Dynamic Non-Parametric Mixture Models and The Recurrent Chinese Restaurant Process |
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| 课程网址: | http://videolectures.net/icml08_xing_dnp/ |
| 主讲教师: | Eric P. Xing |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Dirichlet process mixture models provide a °exible Bayesian framework for estimating a distribution as an in¯nite mixture of simpler distributions that could identify latent classes in the data [1]. However the full exchangeability assumption they employ makes them an unappealing choice for modeling longitudinal data such as text, audio and video streams that can arrive or accumulate as epochs, where data points inside the same epoch can be assumed to be fully exchangeable, whereas across the epochs both the structure (i.e., the number of mixture components) and the parameteriza- tions of the data distributions can evolve and therefore unexchangeable. |
| 关 键 词: | 贝叶斯框架; 完全可交换性; 纵向数据 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 29 |