摘要翻译：
本文讨论了带约束的二元分布的抽样问题。特别地，它提出了一种MCMC方法，该方法从与某个参考状态的指定距离处的所有状态集的分布中提取样本。例如，当参考状态是零的向量时，该算法可以从二进制分布中提取样本，并对活动变量的数量（例如，1的数量）有约束。我们通过统计物理和概率推理的例子来说明对该算法的需求。与以前提出的从具有这些约束的二元分布中采样的算法不同，新算法允许状态空间中的大移动，并倾向于提出它们，使得它们在能量上是有利的。该算法在三种不同难度的玻尔兹曼机器上得到了证明：铁磁伊辛模型（正电位）、学习Gabor滤波器作为电位的受限玻尔兹曼机器和具有挑战性的三维自旋玻璃（正电位和负电位）。
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英文标题：
《Intracluster Moves for Constrained Discrete-Space MCMC》
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作者：
Firas Hamze, Nando de Freitas
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最新提交年份：
2012
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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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：
  This paper addresses the problem of sampling from binary distributions with constraints. In particular, it proposes an MCMC method to draw samples from a distribution of the set of all states at a specified distance from some reference state. For example, when the reference state is the vector of zeros, the algorithm can draw samples from a binary distribution with a constraint on the number of active variables, say the number of 1's. We motivate the need for this algorithm with examples from statistical physics and probabilistic inference. Unlike previous algorithms proposed to sample from binary distributions with these constraints, the new algorithm allows for large moves in state space and tends to propose them such that they are energetically favourable. The algorithm is demonstrated on three Boltzmann machines of varying difficulty: A ferromagnetic Ising model (with positive potentials), a restricted Boltzmann machine with learned Gabor-like filters as potentials, and a challenging three-dimensional spin-glass (with positive and negative potentials).
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PDF链接：
https://arxiv.org/pdf/1203.3484