图 书 馆
非参数变分推理Nonparametric Variational Inference |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_hoffman_variational/ |
| 主讲教师: | Matt Hoffman |
| 开课单位: | Adobe公司 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Unlike most other variational approximations, using multiple kernels allows the approximation to capture multiple modes of the posterior. We demonstrate the e.cacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and MCMC approximations. The method is easy to apply to graphical models for which standard variational methods are difficult to derive. |
| 关 键 词: | variational; nonparametric kernel density estimation; method |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：cxin |
| 阅读次数: | 331 |