Modeling the Discrete Uniform Distribution Under Indeterminacy with Data Generation Algorithms

Abstract

The classical discrete uniform distribution and traditional algorithms for it are not suitable for uncertain environments and do not account for the degree of indeterminacy. To address these gaps, the paper first introduces the Neutrosophic Discrete Uniform Distribution (NDUD) along with its basic properties. Two algorithms are then developed using this distribution by incorporating the degree of indeterminacy. These algorithms can generate data from the discrete uniform distribution while considering different levels of indeterminacy, enabling them to handle imprecise data effectively. Importantly, they also extend the scope of classical statistical algorithms. Simulation studies show that the level of indeterminacy has a significant effect on data generation, and we recommend using these algorithms for generating data in complex or uncertain environments.