Algorithmic generation of imprecise data from uniform and Weibull distributions

Muhammad Aslam; Osama H. Arif; Muhammad Aslam; Osama H. Arif

doi:10.3934/math.2024639

AIMS Mathematics

2024, Volume 9, Issue 5: 13087-13101. doi: 10.3934/math.2024639

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Algorithmic generation of imprecise data from uniform and Weibull distributions

Muhammad Aslam ^,,
Osama H. Arif

Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia

Received: 03 March 2024 Revised: 23 March 2024 Accepted: 29 March 2024 Published: 08 April 2024
MSC : 62A86

This paper introduced the neutrosophic uniform distribution and innovative simulation methods to generate random numbers from the neutrosophic uniform distribution and the neutrosophic Weibull distribution. We introduced simulation methods and algorithms designed to handle indeterminacy for both of these distributions. We provided random numbers generated from both distributions across a range of parameter values and degrees of indeterminacy. Furthermore, we conducted a comparative analysis between the classical simulation method in classical statistics and the neutrosophic simulation method. Our findings reveal that the proposed neutrosophic simulation method generates random numbers of smaller magnitudes compared to the classical simulation method under classical statistics. This observation forms the basis of our conclusion.
- classical simulation,
- neutrosophic simulation,
- random numbers,
- uniform distribution,
- Weibull distribution,
- algorithms
Citation: Muhammad Aslam, Osama H. Arif. Algorithmic generation of imprecise data from uniform and Weibull distributions[J]. AIMS Mathematics, 2024, 9(5): 13087-13101. doi: 10.3934/math.2024639

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Abstract

This paper introduced the neutrosophic uniform distribution and innovative simulation methods to generate random numbers from the neutrosophic uniform distribution and the neutrosophic Weibull distribution. We introduced simulation methods and algorithms designed to handle indeterminacy for both of these distributions. We provided random numbers generated from both distributions across a range of parameter values and degrees of indeterminacy. Furthermore, we conducted a comparative analysis between the classical simulation method in classical statistics and the neutrosophic simulation method. Our findings reveal that the proposed neutrosophic simulation method generates random numbers of smaller magnitudes compared to the classical simulation method under classical statistics. This observation forms the basis of our conclusion.

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