摘要翻译：
定义了用证据编译贝叶斯网络的概念，并给出了一种利用逻辑处理进行基于证据编译的具体方法。该方法在许多应用领域都是实用和有利的--包括最大似然估计、灵敏度分析和MAP计算--我们在遗传连锁分析领域提供了具体的实证结果。我们还证明了该方法适用于不包含确定性的网络，并证明了当应用于含噪-OR网络时，它在经验上包含了quickscore算法的性能。
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英文标题：
《Exploiting Evidence in Probabilistic Inference》
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作者：
Mark Chavira, David Allen, Adnan Darwiche
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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：
  We define the notion of compiling a Bayesian network with evidence and provide a specific approach for evidence-based compilation, which makes use of logical processing. The approach is practical and advantageous in a number of application areas-including maximum likelihood estimation, sensitivity analysis, and MAP computations-and we provide specific empirical results in the domain of genetic linkage analysis. We also show that the approach is applicable for networks that do not contain determinism, and show that it empirically subsumes the performance of the quickscore algorithm when applied to noisy-or networks.
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PDF链接：
https://arxiv.org/pdf/1207.1372