The Monte Carlo simulation is a versatile method for analyzing the behavior of some activities, plans or processes that involve uncertainty. The method was invented by scientists working on the atomic bomb in the 1940s. It uses randomness to obtain random variable estimates, similarly to the gambling process. A Monte Carlo simulation is a parametric procedure, where specific distributional parameters are required before a simulation can begin. In its simplest form, it can be considered as a random number generator which is useful for forecasting, estimation and risk analysis. The Markov Chain Monte Carlo method is more efficient than simple Monte Carlo in obtaining samples for any target distribution, and achieving more general inferential objectives.