摘要翻译：
在贝叶斯网络中，最可能解释(MPE)是给定当前证据的最大概率的完全变量实例化。本文讨论了MPE在单参数变化下的鲁棒性条件的求解问题。具体地说，我们会问这样一个问题：在保持MPE不变的情况下，我们可以在单个网络参数中应用多少变化？我们将描述一个程序，这是第一个此类程序，它计算贝叶斯网络变量中的每个参数在时间O（n exp(w))时的答案，其中n是网络变量的数目，w是它的树宽。
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英文标题：
《On the Robustness of Most Probable Explanations》
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作者：
Hei Chan, 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：
  In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with a highest probability given the current evidence. In this paper, we discuss the problem of finding robustness conditions of the MPE under single parameter changes. Specifically, we ask the question: How much change in a single network parameter can we afford to apply while keeping the MPE unchanged? We will describe a procedure, which is the first of its kind, that computes this answer for each parameter in the Bayesian network variable in time O(n exp(w)), where n is the number of network variables and w is its treewidth.
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PDF链接：
https://arxiv.org/pdf/1206.6819