Using the Inverse Transformation Method to Generate Random Variables that follow the Neutrosophic Uniform Probability Distribution

When conducting the simulation process for any of the systems according to classical logic, we start by generating random numbers belonging to the regular probability distribution on the field [0, 1] using one of the known methods, and then we convert these random numbers into random variables that follow the probability distribution that the system to be simulated works with, the simulation process that we perform it gives specific results that do not take into account the changes that may occur in the work environment of the system, to obtain more accurate results In a previous research, we prepared a study through which we reached random neutrosophic numbers follow the uniform distribution of the neutrosophic on the field with no determination that can be enjoyed by the two parties of the field, one or both together, it may be in the form of a group or a field in another research , we converted these neutrosophic random numbers into neutrosophic random variables that follow the neutrosophic exponential distribution using the opposite conversion method that depends on the cumulative distribution function of the probability distribution by which the system to be simulated works, in this research we have useda method The opposite transformation to generate random variables that follow the neutrosophic uniform distribution and we have reached relationships through which we can convert the neutrosophic random numbers that follow the neutrosophic uniform distribution defined on the domain with the indeterminacy enjoyed by each end of the field, one or the other, into random variables that follow the neutrosophic uniform distribution, which is a classical uniform distribution whose medians are not precisely defined values , one or both may be cognitiven in the form of a set or a domain, so that n take into account all possible cases of mediators while maintaining the condition , and the ;method was illustrated through an applied example and we came up with neutrosophic random variables that follow the uniform distribution that give us more accurate simulation results when used due to the indeterminacy of neutrosophic values.