摘要翻译：
贝叶斯信念网络因其为许多问题提供了紧凑的表示而变得日益突出，而概率推理是适用于这些问题的，并且有一些算法可以利用这种紧凑性。下一步是允许对给定其父变量的条件概率进行紧凑的表示。在本文中，我们提出了这样一种表示，它利用了父语境的语境独立性；哪些变量充当父变量可能取决于其他变量的值。内部表示是根据上下文因子(confactors)，即简单的上下文和表对。上下文变量消除算法基于标准变量消除算法，该算法依次消除非查询变量，但在消除变量时，需要相乘的表可以依赖于上下文。当没有上下文独立性结构可利用时，该算法简化为标准变量消除。我们展示了当有结构可利用时，这是如何比变量消除更有效的。我们解释了为什么这种新方法比以前的方法能够利用更多的结构来进行结构化信任网络推理，以及一种使用树的类似算法。
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英文标题：
《Exploiting Contextual Independence In Probabilistic Inference》
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作者：
D. Poole, N. L. Zhang
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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：
  Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the non-query variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.
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PDF链接：
https://arxiv.org/pdf/1106.4864