摘要翻译：
提出了一种新的基于迭代置信传播(IBP)不动点的聚类累积量展开(CCE)。这种展开式与最近在[1]中引入的循环级数(LS)在精神上是相似的。然而，与后者相比，CCE具有以下重要的性质：1）它定义于任意状态空间；2）它易于推广到广义信念传播的不动点；3）不连通的变量群不会对CCE有贡献；4）扩展的精度在经验上比LS提高。CCE是基于与Kikuchi近似相同的M“Obius变换，但与GBP不同，它不需要存储GBP-簇的信念，也不存在信念更新过程中的收敛问题。
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英文标题：
《A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation》
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作者：
Max Welling, Andrew E. Gelfand, Alexander T. Ihler
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) recently introduced in [1]. However, in contrast to the latter, the CCE enjoys the following important qualities: 1) it is defined for arbitrary state spaces 2) it is easily extended to fixed points of generalized belief propagation (GBP), 3) disconnected groups of variables will not contribute to the CCE and 4) the accuracy of the expansion empirically improves upon that of the LS. The CCE is based on the same M\"obius transform as the Kikuchi approximation, but unlike GBP does not require storing the beliefs of the GBP-clusters nor does it suffer from convergence issues during belief updating.
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PDF链接：
https://arxiv.org/pdf/1210.4916