P-Box CDF区间：具有可量化信息的可靠约束推理
The P-Box CDF-Intervals: Reliable Constraint Reasoning with Quantifiable Information
摘要：
本文提出了一种新的约束域，用于不确定性数据的推理。它扩展了凸建模与p盒的概念，以获得额外的量化信息的数据下落。与现有的方法不同，p-box是一个未知的概率，而不是近似的表示。为了在概率域中采用线性计算，p盒边界是均匀累积分布函数（cdf）。利用p盒cdf区间的推理是一种区间计算，它是在实数域上进行的，然后将其投影到cdf域上。该操作传达由所获得的概率界限表示的附加知识。我们的实现的经验评估表明，以最小的开销，输出的解决方案集实现了一个完整的外壳的数据，其概率分布上的更严格的界限。
英文摘要：
This paper introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are uniform cumulative distribution functions (cdf) in order to employ linear computations in the probabilistic domain. The reasoning by means of p-box cdf-intervals is an interval computation which is exerted on the real domain then it is projected onto the cdf domain. This operation conveys additional knowledge represented by the obtained probabilistic bounds. The empirical evaluation of our implementation shows that, with minimal overhead, the output solution set realizes a full enclosure of the data along with tighter bounds on its probabilistic distributions.
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。
