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摘要翻译：
记录链接和多目标跟踪等任务涉及重建一些观测数据基础上的对象集，对概率推理特别具有挑战性。最近的工作已经用马尔可夫链蒙特卡罗(MCMC)技术和定制的建议分布实现了对这类问题的高效和准确的推理。目前，实现这样的系统需要对特定于特定应用的MCMC状态表示和接受概率计算进行编码。本文的另一种方法是使用通用的概率建模语言（如BLOG）和支持用户提供的建议分发的通用Metropolis-Hastings MCMC算法。我们的算法通过使用MCMC状态来获得灵活性，而MCMC状态只是对可能世界的部分描述；我们给出了部分世界上的MCMC对查询产生正确答案的条件。我们还展示了如何使用上下文特定的贝叶斯网络来识别接受概率中的因素，这些因素需要为给定的提议移动计算。在一个引文匹配任务上的实验结果表明，我们的通用MCMC引擎与特定应用系统相比具有良好的性能。
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英文标题：
《General-Purpose MCMC Inference over Relational Structures》
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作者：
Brian Milch, Stuart Russell
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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：
Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system requires coding MCMC state representations and acceptance probability calculations that are specific to a particular application. An alternative approach, which we pursue in this paper, is to use a general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied proposal distributions. Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which MCMC over partial worlds yields correct answers to queries. We also show how to use a context-specific Bayes net to identify the factors in the acceptance probability that need to be computed for a given proposed move. Experimental results on a citation matching task show that our general-purpose MCMC engine compares favorably with an application-specific system.
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PDF链接：
https://arxiv.org/pdf/1206.6849
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