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切片采样协方差参数的潜在Slice sampling covariance hyperparameters of latent |
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| 课程网址: | http://videolectures.net/nips2010_murray_ssc/ |
| 主讲教师: | Iain Murray |
| 开课单位: | 爱丁堡大学 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown hyperparameters. Integrating over these hyperparameters considers different possible explanations for the data when making predictions. This integration is often performed using Markov chain Monte Carlo (MCMC) sampling. However, with non-Gaussian observations standard hyperparameter sampling approaches require careful tuning and may converge slowly. In this paper we present a slice sampling approach that requires little tuning while mixing well in both strong- and weak-data regimes. |
| 关 键 词: | 蒙特卡罗方法; 计算机科学; 机器学习; 高斯过程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-02：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 39 |