图 书 馆
离散哈密顿蒙特卡罗的连续松弛Continuous Relaxations for Discrete Hamiltonian Monte Carlo |
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| 课程网址: | http://videolectures.net/machine_zhang_continuous_relaxations/ |
| 主讲教师: | Yichuan Zhang |
| 开课单位: | 爱丁堡大学 |
| 开课时间: | 2013-01-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference. Here we show that a general form of the Gaussian Integral Trick makes it possible to transform a wide class of discrete variable undirected models into fully continuous systems. The continuous representation allows the use of gradient-based Hamiltonian Monte Carlo for inference, results in new ways of estimating normalization constants (partition functions), and in general opens up a number of new avenues for inference in difficult discrete systems. We demonstrate some of these continuous relaxation inference algorithms on a number of illustrative problems. |
| 关 键 词: | 离散优化; 近似概率; 高斯积分 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：lxf |
| 阅读次数: | 184 |