摘要翻译：
图形模型的推理和学习是统计学和机器学习中研究较多的问题，在科学和工程中有着广泛的应用。然而，在一般的图形模型中，精确的推理是困难的，这就提出了在一些可处理的图形模型族中寻求对随机变量集合的最佳逼近的问题。在本文中，我们把注意力集中在一类平面伊辛模型上，对这类模型的推理是很容易使用统计物理技术的[Kac和Ward；Kasteleyn]。基于这些技术和最近的平面性测试和平面嵌入方法[Chrobak和Payne]，我们提出了一个简单的贪婪算法来学习最佳的平面伊辛模型，以逼近任意的二元随机变量集合（可能来自样本数据）。给定变量之间所有成对相关的集合，我们选择一个平面图和定义在该图上的最优平面Ising模型来最好地逼近该相关集合。我们在一些仿真和参议院投票记录建模的应用中演示了我们的方法。
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英文标题：
《Learning Planar Ising Models》
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作者：
Jason K. Johnson, Praneeth Netrapalli and Michael Chertkov
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最新提交年份：
2010
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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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：
  Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus our attention on the class of planar Ising models, for which inference is tractable using techniques of statistical physics [Kac and Ward; Kasteleyn]. Based on these techniques and recent methods for planarity testing and planar embedding [Chrobak and Payne], we propose a simple greedy algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. We demonstrate our method in some simulations and for the application of modeling senate voting records.
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PDF链接：
https://arxiv.org/pdf/1011.3494