图 书 馆
贝叶斯学习Bayesian Learning |
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| 课程网址: | http://videolectures.net/mlss05us_ghahramani_bl/ |
| 主讲教师: | Zoubin Ghahramani |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Bayes Rule provides a simple and powerful framework for machine learning. This tutorial will be organised as follows: 1. I will give motivation for the Bayesian framework from the point of view of rational coherent inference, and highlight the important role of the marginal likelihood in Bayesian Occam's Razor. 2. I will discuss the question of how one should choose a sensible prior. When Bayesian methods fail it is often because no thought has gone into choosing a reasonable prior. 3. Bayesian inference usually involves solving high dimensional integrals and sums. I will give an overview of numerical approximation techniques (e.g. Laplace, BIC, variational bounds, MCMC, EP...). 4. I will talk about more recent work in non-parametric Bayesian inference such as Gaussian processes (i.e. Bayesian kernel "machines"), Dirichlet process mixtures, etc. |
| 关 键 词: | 贝叶斯规则; 贝叶斯奥卡姆剃刀; 高维积分 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-09：cjy |
| 阅读次数: | 98 |