ABSTRACT
The rapid development of computer technology in the past two decades, has not only made advances in data analysis and applied statistics, but also in mathematical statistics. Traditionally, mathematical statistics has been dominated by analytical methods using mostly algebraic operations, calculus, probability axioms and algebra, and some techniques in trigonometry and geometry. For the generation of random variables other than continuous uniform variable, the inverse transformation method is most commonly used. In generating a discrete random variable X, the unit interval is divided into several segments. For generation of samples from multivariate distributions, the standard rejection method is usually difficult to apply. The other method for generation of a multivariate distribution is by generating a sequence of random samples from conditional univariate distributions that together can yield the multivariate distribution. Markov chains is a subject in applied probability and is used to study a sequence of random variables.
