图 书 馆
Sequential Bayesian Prediction in the Presence of ChangepointsSequential Bayesian Prediction in the Presence of Changepoints |
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| 课程网址: | http://videolectures.net/icml09_osborne_sbp/ |
| 主讲教师: | Michael Osborne |
| 开课单位: | 牛津大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We introduce a new sequential algorithm for making robust predictions in the presence of changepoints. Unlike previous approaches, which focus on the problem of detecting and locating changepoints, our algorithm focuses on the problem of making predictions even when such changes might be present. We introduce nonstationary co-variance functions to be used in Gaussian process prediction that model such changes, then proceed to demonstrate how to effectively manage the hyperparameters associated with those covariance functions. By using Bayesian quadrature, we can integrate out the hyperparameters, allowing us to calculate the marginal predictive distribution. Furthermore, if desired, the posterior distribution over putative changepoint locations can be calculated as a natural byproduct of our prediction algorithm. |
| 关 键 词: | 顺序算法; 非平稳协方差函数; 高斯过程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 37 |