为了演示该方法，此代码使用蒙特卡罗模拟计算塔（多伦多的 CN 塔）的体积。

实验内容： 1、 了解matlab中各种常见随机数的生成； 2、 掌握一维，高维的数值积分的monte carlo 方法； 实验要求： 1、 研究monte carlo 求积精度与样本容量的关系； 2、 比较monte carlo 方法与代数求积法的精度差异；

Introducing Monte Carlo Methods with R Introducing Monte Carlo Methods with R (Use R) By Christian P. Robert, George Casella Publisher: Springer Number Of Pages: 302 Publication Date: 2009-12-14 ISBN-10 / ASIN: 1441915753 ISBN-13 / EAN: 9781441915757 Product Description: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory,

IsingModel-蒙特卡洛 日语中的Ising模型的蒙特卡洛（MC）仿真简介。 Ising2d_MC 用日语记录Ising模型和MC模拟（Metropolis方法和热浴方法）。 使用Python的各向同性Ising mocdel的MC模拟代码（计算磁化强度，比热，Binder累积量和相关函数）。 Python代码中的注释为英文。 Ising2d_各向异性 使用Python（计算磁化强度）的各向异性Ising模型的MC模拟代码。

EGSnrc toolkit for Monte Carlo simulation of ionizing radiation transport

介绍Monte Carlo方法的一些理论

用MonteCairo方法通过matlab对薄膜外延生长进行了计算机模拟。模型中引入Morse势描述粒子间的相互作用，考虑粒子的沉积、吸附粒子的扩散和蒸发3个过程。研究了粒子间相互作用范围α和允许粒子行走的最大步数对薄膜生长形貌的影响。结果表明：在不同的6c值下，随粒子行走步数的增加，薄膜的生长都经历了从分散、分形、混合到团聚的过程；在同一最大步数下，α值越大，薄膜越易趋向于分散生长。

窄禁带半导体n-InAs THz辐射机理的Monte Carlo研究.pdf

monte carlo随机数发生器 软件MATLAB 服从正态分布

临时制TSP 这是使用蒙特卡洛树搜索（MCTS）解决旅行商问题（TSP）的源代码。 纸 如果您想了解更多详细信息，请参阅我们的论文“通过蒙特卡罗树搜索TSP的扩大邻域目标抽样” 。 依存关系 gcc> = 4.8.5 计算平台：Linux 快速开始 为了使用MCTS解决具有20个节点的TSP实例： cd $download -dir cd TSP-20-50-100 bash solve-20.sh 32 用法 数据集 我们的模型分别在两个数据集TSP-20-50-100和TSPLib上进行了测试，可从以下网站下载该数据集： 多线程 如果更快地解决TSP实例，则可以充分利用CPU。 默认情况下，我们基于32个线程来处理它们： cd $download -dir cd TSP-20-50-100 bash solve-20.sh 32 顺便说一下，我们的多线程方案仅适用于TSP-2