Inverse Transformation to Generate Neutrosophic Random Variables Following Weibull and Geometric Distributions

Abstract

In practical applications, we encounter many systems that cannot be studied directly, and the reason for this is due to the nature of the system or to the high cost. Therefore, we resort to the simulation process, which depends on conducting the study on systems similar to real systems, and then projecting these results, if they are appropriate, onto the system. The real system. The simulation process depends on generating a series of random numbers that follow a uniform probability distribution in the field [0, 1], then converting these random numbers into random variables that follow the probability distribution in which the system to be simulated operates. One of the most important conversion methods is the conversion method. The opposite. This method is used for probability distributions in which we can obtain a function inverse of its cumulative distribution function. In two previous researches, we used this method to generate neutrosophic random variables that follow a uniform distribution in the field [a, b] and the exponential distribution. In this research, we present a valuable study to clarify how to use this method. The method for generating neutrosophic random variables follows the Weibull distribution and the geometric distribution, based on what was presented in the classic study and in the research on neutrosophic random number generation.