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二进制无向图模型中BP收敛的边界和估算Bounds and estimates for BP convergence on binary undirected graphical models |
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| 课程网址: | http://videolectures.net/oiml05_joris_becbu/ |
| 主讲教师: | Joris Mooij |
| 开课单位: | 拉德布德大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Belief Propagation (BP) has become a popular method for inference on graphical models. Accurate approximations for intractable quantities (e.g. single-node marginals) can be obtained within rather modest computation times. However, for large interaction strengths (i.e. potentials that are highly dependent on their arguments) or densely connected graphs, BP can fail to converge. This can be remedied by damping the iteration equations, using sequential update schemes or using entirely different algorithms such as double-loop algorithms, that directly minimize the Bethe free energy. However, the value of this approach is questionable, since there is empirical evidence that failure of convergence of BP often indicates low quality of the Bethe approximation. |
| 关 键 词: | 图形化模型推理方法; 信仰传播; 迭代方程; BP收敛 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-11：liush |
| 阅读次数: | 73 |