Neutrosophical Rejection and Acceptance Method for the Generation of Random Variables

Abstract

: Simulation has become a modern-day tool that helps us study many systems that its results could
not have been studied or predicted through the work of these systems over time. The simulation process
depends on generating a series of random numbers that are subject to a uniform probability distribution on the
field [0,1], and then converting these random numbers to random variables that follow the probability
distribution in which the system to be simulated, there are several methods that can be used to carry out the
conversion.
In previous research, the inverse transformation method has been studied according to the Neutrosophic logic
and we have reached Neutrosophic random variables, by using them we get a more accurate simulation of any
system we want to simulate. It should be noted that the inverse transformation can be used if the cumulative
distribution function has an inverse function, but if the system we want to simulate works according to a
probability distribution, and the inverse function of the cumulative distribution function cannot be found, then
we use other methods.
In this research, we are examining a study that enables us to generate Neutrosophical random variables that
follow probability distributions based on Neutrosophical random numbers that follow for the uniform
distribution, using the rejection and acceptance method, which depends on the largest value taken by the
probability density function of the distribution in which the system to be simulated operates on its definition.