Neutrosophic Binomial Distribution and Algorithm for Managing Uncertainty in Statistical Modeling

Abstract

This paper presents the neutrosophic binomial distribution, an innovative method for addressing uncertainty in statistical modeling. It explores the properties of neutrosophic random variables and introduces two algorithms that leverage the neutrosophic binomial distribution to generate imprecise data from a conventional binomial distribution. Through extensive simulations with varying parameters, the paper reveals the distinctive characteristics of binomial data generated under uncertainty, in contrast to deterministic settings. The study also highlights practical applications of the proposed neutrosophic binomial distribution. Additionally, it identifies future research directions, including the integration of these algorithms into existing software tools to enhance data generation in uncertain environments.