图 书 馆
现代贝叶斯非参数：超越dirichlet和gaussian过程Modern Bayesian Nonparametrics: beyond Dirichlet and Gaussian processes |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_ghahramani_bayesian/ |
| 主讲教师: | Zoubin Ghahramani |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Nonparametrics plays an important role in Bayesian modelling: nonparametric models are flexible, realistic and by providing good coverage can guard against model inadequacy. Modern Bayesian nonparametrics builds on decades of research on Dirichlet and Gaussian processes to develop new models for complex data sources. I will briefly cover some examples of our recent work in this area: models for overlapping clustering and sparse arrays, probabilistic models of social and biological networks, diffusion tree models for hierarchical clustering, and models for covariance and volatility based on copulas and generalised Wishart processes. I will end on some discussion of limitations, links to classical nonparametrics, and directions for theory. |
| 关 键 词: | 非参数; 贝叶斯建模; 概率模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-08：lxf |
| 阅读次数: | 55 |