图 书 馆
高斯过程点过程的可扩展非参数贝叶斯推断Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes |
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| 课程网址: | http://videolectures.net/icml2015_kom_samo_gaussian_processes/ |
| 主讲教师: | Yves-Laurent Kom Samo |
| 开课单位: | 牛津曼定量金融研究所 |
| 开课时间: | 2015-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we propose an efficient, scalable non-parametric Gaussian process model for inference on Poisson point processes. Our model does not resort to gridding the domain or to introducing latent thinning points. Unlike competing models that scale as O(n3) over n data points, our model has a complexity O(nk2) where k << n. We propose a MCMC sampler and show that the model obtained is faster, more accurate and generates less correlated samples than competing approaches on both synthetic and real-life data. Finally, we show that our model easily handles data sizes not considered thus far by alternate approaches. |
| 关 键 词: | 非参数高斯过程; 竞争模型; 泊松点过程 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-11-06：chenjy |
| 最后编审: | 2022-11-06：chenjy |
| 阅读次数: | 52 |