使用定性关系来界定概率分布
Using Qualitative Relationships for Bounding Probability Distributions
摘要：
我们利用定性的概率关系的变量计算边界的条件概率分布的兴趣在贝叶斯网络。使用定性关系的符号，我们可以实现保证将兴趣分布约束在期望方向上的抽象操作。通过评估增量改进的近似网络，我们的算法得到单调收紧的界限，收敛到精确的分布。对于超模效用函数，紧缩界也单调地减少了可容许决策方案的集合。
英文摘要：
We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operations that are guaranteed to bound the distributions of interest in the desired direction. By evaluating incrementally improved approximate networks, our algorithm obtains monotonically tightening bounds that converge to exact distributions. For supermodular utility functions, the tightening bounds monotonically reduce the set of admissible decision alternatives as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
