英国论文代写：MC计算方法
一类广泛的计算算法，是依赖于重复过程的随机抽样的目的是获得数值结果被称为Monte Carlo（MC）方法。这种技术在物理和数学中被广泛使用，在这种情况下，当它是非常困难的使用任何其他的数学技术。应用蒙特卡罗是最重要的，在三个不同的问题类是不同的性质，即：优化，数值积分，和生成的概率分布。许多自由度耦合与Monte Carlo仿真metgods在物理学领域的相关。流体，强耦合的固体，无序材料和细胞结构的字段是高度依赖于蒙特卡洛模拟方法。
在其他领域的科学，当它是很难模拟的现象，通过应用传统的建模技术，它是依赖于蒙特卡洛模拟。当输入存在巨大的不确定性时，MC方法具有重要意义。这种不确定性在商业风险的计算、定积分的评估和复杂边界条件的求解中非常普遍。预测失败也做了空间和石油勘探领域的问题和成本超支的蒙特卡罗模拟的基础上，它已被观察到，在准确地预测这种技术是成功的（Doucet et al，2001）。
这一技术的现代版本是在公元1940年由Stanislaw Ulam发明的在他工作的核武器项目。在突破后，立即由Monte Carlo发明的程序的重要性是由世界各地的主要科学家实现。
对数正态分布表示的输出数据，提供了一个正倾斜的图形作为一个结果的散射图。分布的一个重要性质是不小于零的值被假定为分布。以下是股票收益对数正态分布的蒙特卡罗模拟结果。结果是通过使用1000条路径。
英国论文代写：MC计算方法
A broad class of computational algorithm that is dependent upon repeated processes of random sampling with an aim to acquire numerical results is known as Monte Carlo (MC) method. This technique is widely used in physics and mathematics in the cases when it is extremely hard to use any other mathematical technique. Application of Mote Carlo is of prime importance in three different problem classes that are of distinct nature namely: optimization, numerical integration, and generation of probability distributions. Many coupled degrees of freedom are associated with Monte Carlo simulation metgods in the field of physics. The fields of fluid, strongly coupled solids, disordered material and cellular structures are highly dependant upon Monte Carlo simulation methods.
In other fields of sciences when it is hard to model a phenomenon by applying traditional modeling techniques it is preferred to rely on Monte Carlo simulation. When there is huge uncertainty existing in the inputs the MC method is of significant importance. Such uncertainties are very common in calculation of business risk, evaluation of definite integrals, and solution of complicated boundary conditions. Predictions of failure are also done in the field of space and oil exploration problems and costs overruns on the basis of Monte Carlo Simulation and it has been observed that this technique remains successful in making accurate predictions (Doucet et al, 2001).
The modern version of this technique was invented in the year 1940 by Stanislaw Ulam in the time when he was working on the project of nuclear weapons. Immediately after the breakthrough the importance of the program invented by Monte Carlo was realized by the major scientists all over the world.
The log normal distribution is expressed by an output data that provides a positively skewed figure as a result of scatter plot. An important property of the distribution is that no value of less than zero is assumed by the distribution. Following are the results of Monte Carlo Simulation for a log normal distribution of stock returns. The result is obtained by using 1000 paths.