图 书 馆
高维统计：预测、关联和因果推理High-dimensional Statistics: Prediction, Association and Causal Inference |
|
| 课程网址: | http://videolectures.net/nips2010_buhlmann_hds/ |
| 主讲教师: | Peter Bühlmann |
| 开课单位: | 苏黎世联邦理工学院 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This tutorial surveys methodology and theory for high-dimensional statistical inference when the number of variables or features greatly exceeds sample size. Particular emphasis will be placed on problems of model and feature selection. This includes variable selection in regression models or estimation of the edge set in graphical modeling. While the former is concerned with association, the latter can be used for causal analysis. In the high-dimensional setting, major challenges include designing computational algorithms that are feasible for large-scale problems, assigning statistical error rates (e.g., p-values), and developing theoretical insights about the limits of what is possible. We will present some of the most important recent developments and discuss their implications for prediction, association analysis and some exciting new directions in causal inference. |
| 关 键 词: | 高维统计参数; 特征选择; 图形建模 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-25：cwx |
| 阅读次数: | 60 |