图 书 馆
加速自适应马尔可夫链配分函数计算Accelerated Adaptive Markov Chain for Partition Function Computation |
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| 课程网址: | http://videolectures.net/nips2011_ermon_computation/ |
| 主讲教师: | Stefano Ermon |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat histogram sampling technique by significantly reducing the number of "null moves" in the chain, while maintaining asymptotic convergence properties. Our experiments show that our method converges quickly to highly accurate solutions on a range of benchmark instances, outperforming other state-of-the-art methods such as IJGP, TRW, and Gibbs sampling both in run-time and accuracy. We also show how obtaining a so-called density of states distribution allows for efficient weight learning in Markov Logic theories. |
| 关 键 词: | 马尔可夫链蒙特卡罗算法; 马尔可夫逻辑理论; 采样技术 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 34 |