图 书 馆
非参数非平稳性的高斯过程产品模型Gaussian Process Product Models for Nonparametric Nonstationarity |
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| 课程网址: | http://videolectures.net/icml08_stegle_gpp/ |
| 主讲教师: | Oliver Stegle |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2008-07-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covariance functions can be difficult to specify and require detailed prior knowledge of the nonstationarity. We propose the Gaussian process product model (GPPM) which models data as the pointwise product of two latent Gaussian processes to nonparametrically infer nonstationary variations of amplitude. This approach differs from other nonparametric approaches to covariance function inference in that it operates on the outputs rather than the inputs, resulting in a significant reduction in computational cost and required data for inference, while improving scalability to high-dimensional input spaces. We present an approximate inference scheme using Expectation Propagation. This variational approximation yields convenient GP hyperparameter selection and compact approximate predictive distributions. |
| 关 键 词: | 高斯过程; 非平稳协方差函数; 近似预测分布 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 69 |