Bounding probabilistic relationships in Bayesian networks using qualitative influences: methods and applications

Chao-Lin Liu; Michael P. Wellman

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Bounding probabilistic relationships in Bayesian networks using qualitative influences: methods and applications

机译：使用定性影响的贝叶斯网络中的概率关系：方法和应用

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We present conditions under which one can bound the probabilistic relationships between random variables in a Bayesian network by exploiting known or induced qualitative relationships. Generic strengthening and weakening operations produce bounds on cumulative distributions, and the directions of these bounds are maintained through qualitative influences. We show how to incorporate these operations in a state-space abstraction method, so that bounds provably tighten as an approximate network is refined. We apply these techniques to qualitative tradeoff resolution demonstrating an ability to identify qualitative relationships among random variables without exhaustively using the probabilistic information encoded in the given network. In an application to path planning, we present an anytime algorithm with run-time computable error bounds.

机译：我们提出了一种条件，在该条件下，可以利用已知或诱发的定性关系来限制贝叶斯网络中随机变量之间的概率关系。一般的强化和削弱操作会在累积分布上产生边界，并且通过定性影响来保持这些边界的方向。我们展示了如何将这些操作合并到状态空间抽象方法中，从而随着精确网络的完善，可证明地限制了边界。我们将这些技术应用于定性权衡解决方案，从而证明了无需完全使用给定网络中编码的概率信息即可识别随机变量之间的定性关系的能力。在路径规划的应用中，我们提出了一种具有运行时可计算误差范围的随时算法。

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来源
《International Journal of Approximate Reasoning》 |2004年第1期|p.31-73|共43页
作者
Chao-Lin Liu; Michael P. Wellman;
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作者单位

National Chengchi University, 64 Sec. 2, Chih-Nan Road, Taipei 11605, Taiwan;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类高等数学;
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Bounding probabilistic relationships in Bayesian networks using qualitative influences: methods and applications

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