当我们知道具有区间或模糊不确定性的概率时计算统计特征:计算复杂度

G. Xiang, J. W. Hall
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引用次数: 0

摘要

在传统统计学中,我们通常假设我们知道确切的概率分布。在实践中,我们通常只知道具有区间不确定性的概率。考虑这种不确定性的主要重点是在我们知道具有区间不确定性的累积分布函数(cdf)的情况下。然而,在某些情况下,我们知道概率密度函数(pdf)具有区间不确定性。我们表明,在这种情况下,一些统计特征的精确范围可以有效地计算出来。令人惊讶的是,对于其他一些特征,区间值cdf可以有效解决的类似统计问题在区间值pdf中变得难以计算(NP-hard)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing Statistical Characteristics When We Know Probabilities with Interval or Fuzzy Uncertainty: Computational Complexity
In traditional statistics, we usually assume that we know the exact probability distributions. In practice, we often only know the probabilities with interval uncertainty. The main emphasis on taking this uncertainty into account has been on situations in which we know a cumulative distribution function (cdf) with interval uncertainty. However, in some cases, we know the probability density function (pdf) with interval uncertainty. We show that in this situations, the exact range of some statistical characteristics can be efficiently computed. Surprisingly, for some other characteristics, similar statistical problems which are efficiently solvable for interval-valued cdf become computationally difficult (NP-hard) for interval-valued pdf.
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