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摘要翻译：
虽然许多算法都采用不同的方法和原理来构造贝叶斯网络结构，但它们都只采用两种方法：基于独立性准则的方法和基于评分函数和搜索过程的方法（尽管有些方法将两者结合起来）。在Score+搜索范式中，占主导地位的方法使用有向无环图（DAG）空间中的局部搜索方法，其中定义可以应用的基本修改（局部变化）的通常选择是弧加法、弧删除和弧反转。本文提出了一种新的局部搜索方法，它使用不同的搜索空间，并考虑了网络结构之间的等价概念：受限无环部分有向图（RPDAGs）。这样，减少了搜索空间的不同配置的数量，从而提高了效率。此外，尽管给定搜索方法的性质，最终结果必然是局部最优，但新搜索空间的拓扑结构避免了对弧的方向做出早期决定，可能有助于找到比在DAG空间中搜索得到的更好的局部最优。文中还给出了该搜索方法在几个测试问题上的详细评估结果，包括著名的报警监控系统。
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英文标题：
《Searching for Bayesian Network Structures in the Space of Restricted
Acyclic Partially Directed Graphs》
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作者：
S. Acid, L. M. de Campos
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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：
Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.
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PDF链接：
https://arxiv.org/pdf/1107.0019
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