摘要翻译:
概率网络的灵敏度分析所揭示的灵敏度通常依赖于输入的证据。因此,对于一个现实生活中的网络,分析被执行多次,有不同的证据。虽然灵敏度分析的有效算法是存在的,但由于观测结果的可能组合范围很大,所以完整的分析往往是不可行的。在本文中,我们提出了一种方法来研究灵敏度是不变的证据输入。我们的方法建立在这样一个思想基础上,即建立一个界限,在这个界限之间可以改变一个参数,而不会引起一个感兴趣的变量的最可能值的改变。
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英文标题:
《Evidence-invariant Sensitivity Bounds》
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作者:
Silja Renooij, Linda C. van der Gaag
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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:
The sensitivities revealed by a sensitivity analysis of a probabilistic network typically depend on the entered evidence. For a real-life network therefore, the analysis is performed a number of times, with different evidence. Although efficient algorithms for sensitivity analysis exist, a complete analysis is often infeasible because of the large range of possible combinations of observations. In this paper we present a method for studying sensitivities that are invariant to the evidence entered. Our method builds upon the idea of establishing bounds between which a parameter can be varied without ever inducing a change in the most likely value of a variable of interest.
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PDF链接:
https://arxiv.org/pdf/1207.4170