Back to Search
Start Over
Uncertainty-driven generation of neutrosophic random variates from the Weibull distribution.
- Source :
- Journal of Big Data; 12/20/2023, Vol. 10 Issue 1, p1-17, 17p
- Publication Year :
- 2023
-
Abstract
- Objective: This paper aims to introduce an algorithm designed for generating random variates in situations characterized by uncertainty. Method: The paper outlines the development of two distinct algorithms for producing both minimum and maximum neutrosophic data based on the Weibull distribution. Results: Through comprehensive simulations, the efficacy of these algorithms has been thoroughly assessed. The paper includes tables presenting neutrosophic random data and an in-depth analysis of how uncertainty impacts these values. Conclusion: The study's findings demonstrate a noteworthy correlation between the degree of uncertainty and the neutrosophic minimum and maximum data. As uncertainty intensifies, these values exhibit a tendency to decrease. [ABSTRACT FROM AUTHOR]
- Subjects :
- WEIBULL distribution
DATA analysis
Subjects
Details
- Language :
- English
- ISSN :
- 21961115
- Volume :
- 10
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Big Data
- Publication Type :
- Academic Journal
- Accession number :
- 174342506
- Full Text :
- https://doi.org/10.1186/s40537-023-00860-y