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近似再加权的广义传播
Approximations with Reweighted Generalized Belief Propagation |
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| 课程网址: |
http://videolectures.net/oiml05_wiegerinck_argbp/
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| 主讲教师: |
Wim Wiegerinck
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| 开课单位: |
拉德堡德大学
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| 开课时间: |
2007-02-25
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| 课程语种: |
英语
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| 中文简介: |
在（Wainwright等人，2002）中，已经开发了任意无向图形模型的对数分区函数的新的一般上界类。通过采用易处理分布的凸组合来构造该界限。到目前为止公布的实验结果集中于树形结构分布的组合，导致凸起的Bethe自由能，其通过树重新加权的置信传播算法最小化。这类近似的一个有利特性是保证增加近似的复杂性以提高精度。缺乏这种保证在标准的广义信念传播中是臭名昭着的。我们通过结合树的组合来增加近似分布的复杂性，导致凸起的Kikuchi自由能，其通过重新加权的广义信念传播被最小化。给出了Ising网格以及完全连接的Ising模型的实验结果，说明了重新加权方法在近似推断中的优点和缺点。
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| 课程简介: |
In (Wainwright et al., 2002) a new general class of upper bounds on the log partition function of arbitrary undirected graphical models has been developed. This bound is constructed by taking convex combinations of tractable distributions. The experimental results published so far concentrates on combinations of tree-structured distributions leading to a convexified Bethe free energy, which is minimized by the tree-reweighted belief propagation algorithm. One of the favorable properties of this class of approximations is that increasing the complexity of the approximation is guaranteed to increase the precision. The lack of this guarantee is notorious in standard generalized belief propagation. We increase the complexity of the approximating distributions by taking combinations of junction trees, leading to a convexified Kikuchi free energy, which is minimized by reweighted generalized belief propagation. Experimental results for Ising grids as well as for fully connected Ising models are presented illustrating advantages and disadvantages of the reweighting method in approximate inference.
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| 关 键 词: |
无向图形模型; 树形结构; 近似再加权
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-08：cxin
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| 阅读次数: |
52
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