A History of Distribution Sampling Prior to the Era of the Computer and its Relevance to Simulation
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计算机时代以前的分布抽样史及其对模拟的意义
- 研究问题
- 计算机出现前的经验抽样或分布抽样积累了哪些方法与局限，这些经验在现代模拟中能直接继承到什么程度？
- 方法与思路
- 作者先回顾统计学中的分布抽样历史，再讨论其与模拟的联系，区分随机生成技术的可迁移性及其他理论的局限。
- 材料陈述的贡献
- 摘要强调，随机生成技术可直接用于模拟，但时间序列之间缺乏独立性、非平稳性和大量参数会使模拟面临额外困难。
- 阅读关注点
- 应核对历史材料的范围、经验抽样与模拟的概念边界，以及独立性、平稳性和参数数量为何限制既有理论的适用。
- 阅读边界
- 仅据版本未注明的索引摘要；这是历史与方法论讨论，未提供处理所述困难的新定理或算法，不能推断刊本变化。
原文摘要
The use of simulation, as a technique for attacking difficult problems, has increased greatly with the availability of the digital computer. This is illustrated by the large number of references in Shubik's (1960) bibliography2 and in the large number of studies published since then. Simulation is essentially an extension of a technique known as empirical sampling, or distribution sampling, which has been used in the field of statistics for many years. The limitations of the technique, which are well known to statisticians, are apparently not as well known, or at least not as well recognized, by those using simulation today. The first part of this paper contains an historical survey of distribution sampling as used by statisticians. The material was originally prepared in 1953 and is reproduced here in slightly revised form to bring this history to the attention of present day simulators in order that the lessons that can be learned from this part can more readily be incorporated in the development of methodology today. The second part of this paper discusses the relevance of empirical sampling to the present day state of the art of simulation. The technique of generating random members, developed for empirical sampling can be applied directly to simulation. However in other aspects simulation is more difficult than empirical sampling and here the theory of distribution sampling does not have much to offer. The difficulties are due to lack of independence among time series, non-stationarity of the time series, and the large number of parameters.