Some further methods of discretizing the exponential distribution

I. Kabak
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Abstract

One method for discretizing the exponential distribution such that the discrefized values bear a resemblance to the continuous distribution is as follows. The density function will first be divided into N equiprobable intervals each of area 1/N. Some measure of central tendency of each interval will define the equiprobable values. In a previous study [1] the effects of using the mean and the median of the equiprobable intervals was noted. The measure of effectiveness used involved a comparison of the moments of the delay distribution for a simple queueing system based upon these measures of central tendency and known theoretical results. This paper will present the results of two extensions of the previously cited study [1]. It was shown that when using the mean of the equiprobable intervals one obtained “better” i.e., less biased, results for the moments of the delay distribution under question than when the medians of the intervals were used. Although these results were very good, on some subjective measure, a bias does exist for all higher moments of the delay distribution. One way to reduce this bias is to consider different measures of each equiprobable interval. This paper shows the results of using the rth root of the rth moment of each interval, corrected for the mean. A second concern arises when there is a limited amount of storage space in the digital computer for the discretized points.
指数分布离散化的进一步方法
一种使指数分布离散化,使离散值与连续分布相似的方法如下。首先将密度函数划分为N个等概率区间,每个区间的面积为1/N。每个区间的集中趋势的某种度量将定义等概率值。在先前的研究[1]中,注意到使用等概率区间的平均值和中位数的影响。使用的有效性度量涉及到基于这些集中趋势度量和已知理论结果的简单排队系统的延迟分布的时刻的比较。本文将介绍对先前引用的研究[1]的两个扩展的结果。结果表明,当使用等概率区间的平均值时,所讨论的延迟分布的矩比使用区间的中位数时得到的结果“更好”,即偏差更小。虽然这些结果非常好,但在某些主观测量上,延迟分布的所有较高时刻确实存在偏差。减少这种偏差的一种方法是考虑每个等概率区间的不同度量。本文展示了使用每个区间的第n阶矩的第n次方根,对平均值进行校正的结果。当数字计算机中存储离散点的空间有限时,第二个问题就出现了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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