Pseudo-random sequences, quasi-random sequences for simulation

用于模拟的伪随机序列、准随机序列

• 批准号: 298421-2006
• 负责人: Pollanen, Marco
• 金额: $ 0.44万
• 依托单位: Trent University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=396114
• 关键词: Pseudo; random; sequences; quasi; simulation

Mathematical models arising in many applied sciences are too complex to solve directly, and so must be solved using a numerical approximation. Unfortunately, as the number of variables in a problem increases, most numerical approximation techniques become ineffective, since the time required to reach a prescribed level of accuracy increases exponentially. Monte Carlo methods are a class of sampling techniques in which one attempts to solve a problem by generating suitable random numbers and observing the fraction of the numbers obeying some property. They seem to perform well for moderate to high-dimensional problems. Many Monte Carlo problems ultimately require non-uniform samples. The primary focus of this research is finding new methods to generate multivariate non-uniform pseudo-random and quasi-random samples with properties desirable for use with Monte Carlo simulations.

在许多应用科学中出现的数学模型太复杂而无法直接求解，因此必须使用数值近似来求解。不幸的是，随着问题中变量数量的增加，大多数数值逼近技术变得无效，因为达到规定精度水平所需的时间呈指数级增加。蒙特卡罗方法是一类抽样技术，其中试图通过生成合适的随机数并观察服从某些属性的数字的分数来解决问题。他们似乎表现良好的中度到高维的问题。许多蒙特卡罗问题最终需要非均匀样本。本研究的主要重点是寻找新的方法来产生多变量非均匀伪随机和准随机样本的性能理想的蒙特卡洛模拟使用。