摘要翻译：
本文提出了一种新的贝叶斯网络抽样方法，该方法只对变量的子集进行抽样，并对其馀变量进行精确的推理。割集抽样是贝叶斯网络中Rao-Blackwellisation原理在抽样中的一种网络结构开发应用。它通过利用基于内存的推理算法来提高收敛性。它也可以看作是Pearl提出的精确割集条件化算法的任意时间近似。当抽样变量构成贝叶斯网络的一个循环割集时，更一般地，当以观察到的抽样变量为条件的网络图的诱导宽度以常数W为界时，割集抽样可以有效地实现。我们在一系列基准上实证证明了该方案的好处。
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英文标题：
《Cutset Sampling for Bayesian Networks》
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作者：
B. Bidyuk, R. Dechter
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最新提交年份：
2011
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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：
  The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the networks graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks.
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PDF链接：
https://arxiv.org/pdf/1110.2740