图 书 馆
关于从样本中学习分布On Learning Distributions from their Samples |
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| 课程网址: | https://videolectures.net/videos/colt2015_kamath_learning_distrib... |
| 主讲教师: | Sudeep Kamath |
| 开课单位: | 信息不详。欢迎您在右侧留言补充。 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | One of the most natural and important questions in statistical learning is how well a distribution can be approximated from its samples. Surprisingly, this question has so far been resolved for only a few approximation measures, for example the KL-divergence, and even then the answer is ad hoc and not well understood. We resolve the question for three more important approximation measures, $\ell_1$, $\ell_2^2$, and $\chi^2$, and if the probabilities are bounded away from zero, we resolve the question for all smooth $f$-divergence approximation measures, thereby providing a coherent understanding of the rate at which a distribution can be approximated from its samples. |
| 关 键 词: | 统计学：样本近似：近似肚度量 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-28：zsp |
| 最后编审: | 2025-03-28：zsp |
| 阅读次数: | 6 |