This work aims to provide an environment for all users who are beginner in the context of the statistical simulation approaches. These techniques are known as the Monte Carlo methods as a whole nowadays. Indeed, the Monte Carlo, as a statistical simulation technique, itself involves the Markov chain Monte Carlo that attracts the attention of researchers from a wide variety of study fields. One may see the Markov chain Monte Carlo as statistical simulation approaches that work based on the iterative algorithms and so the others that are not based on iterative algorithm are the Monte Carlo approaches. We would recommend the reader(s) to learn the elementary undergraduate courses in calculus, probability, and statistics before studying or applying this report for practical purposes. The required topics may include, but not limited to, concept of mathematical function, limit, derivative, partial derivative, simple integrals, probability axioms, discrete and continuous random variables, probability distributions, concept of central tendency and variance, multivariate probability distributions, functions of random variables, and the central limit theorem (CLT).

翻译：本研究旨在为统计模拟方法领域的初学者提供一个学习环境。这些技术如今统称为蒙特卡洛方法。事实上，蒙特卡洛作为一种统计模拟技术，其本身包含的马尔可夫链蒙特卡洛方法已吸引了来自广泛研究领域学者的关注。我们可以将马尔可夫链蒙特卡洛视为基于迭代算法的统计模拟方法，而不基于迭代算法的其他方法则属于蒙特卡洛方法。建议读者在研读本报告或将其实践应用前，先学习微积分、概率论与数理统计的本科基础课程。所需知识包括但不限于：数学函数概念、极限、导数、偏导数、简单积分、概率公理、离散与连续随机变量、概率分布、集中趋势与变异概念、多元概率分布、随机变量函数以及中心极限定理。