图 书 馆
高斯过程的非参数贝叶斯密度建模Nonparametric Bayesian Density Modeling with Gaussian Processes |
|
| 课程网址: | http://videolectures.net/icml08_adams_nbd/ |
| 主讲教师: | Ryan Prescott Adams |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process. |
| 关 键 词: | 高斯过程密度采样器; 生成模型; 非参数贝叶斯密度估计 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-17：lxf |
| 阅读次数: | 112 |