Mathematical Sciences: Random Variables and Computers

数学科学：随机变量和计算机

• 批准号: 9206972
• 负责人: George Marsaglia
• 金额: $ 18.6万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-15 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9206972&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Random; Variables; Computers

The generating and testing of random variables has provided some of the most widely used methods for Monte Carlo simulation. Several new methods have been developed for new kinds of computer architectures, notably the subtract-with-borrow method that is very fast, requires few memory locations, has extremely long periods and provable uniformity. Current efforts are directed toward methods that will take advantage of massively parallel systems, new mass storage devices and other supercomputer features so that software for statistical computing can keep up with and take advantage of the remarkable advances in hardware. Many computer simulation studies call for random input at various point because of uncertainties or complexities of the underlying models. The construction of "pseudo-random" numbers can be sufficiently sophisticated to give workable substitutes for true random observations from a natural process. At the same time these generated numbers are readily available and can, when needed be reproduced exactly for testing purposes or to compare two different computing approaches to the solution of extremely large and complicated mathematical problems.

随机变量的产生和测试为蒙特卡罗模拟提供了一些最广泛使用的方法。为新的计算机体系结构开发了几种新的方法，特别是借入减方法，它非常快，需要很少的存储单元，具有极长的周期和可证明的一致性。目前的努力针对的是利用大规模并行系统、新的大容量存储设备和其他超级计算机特征的方法，以便统计计算软件能够跟上并利用硬件的显著进步。由于基础模型的不确定性或复杂性，许多计算机模拟研究要求在不同的点上随机输入。“伪随机”数的构造可以足够复杂，从而为来自自然过程的真实随机观测提供可行的替代品。同时，这些生成的数字很容易获得，在需要时可以准确地复制出来，用于测试或比较两种不同的计算方法，以解决非常大和复杂的数学问题。