Random number generation and Monte Carlo methods
Random number generation and Monte Carlo methods
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DOI：
10.1007/978-1-4757-2960-3
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发表时间：
1998
影响因子：
15
通讯作者：
J. Gentle
中科院分区：
文献类型：
--
作者：
J. Gentle
在过去的几年里，蒙特卡罗方法和模拟在所有科学中的作用都在增加。这些方法是计算物理、计算化学和其他计算科学迅速发展的子学科的核心。随着计算机能力的不断增强和模拟方法的不断发展，人们认识到计算是与理论和传统实验一起推动自然科学发展的第三种方法。蒙特卡洛也是计算统计学的基本工具。蒙特卡罗或模拟方法的核心是随机数生成。随机数的生成也是许多标准统计方法的核心。大多数分析中所需的随机抽样通常由计算机完成。由于蒙特卡罗方法，贝叶斯分析中所需的计算变得可行。这导致了贝叶斯统计的更广泛的应用，这反过来又导致了新的蒙特卡罗方法的发展，并改进了现有的随机数生成程序。
The role of Monte Carlo methods and simulation in all of the sciences has in creased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, compu tational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recog nition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statis tical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation.