Uncertainty-driven generation of neutrosophic random variates from the Weibull distribution

Uncertainty-driven generation of neutrosophic random variates from the Weibull distribution

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 distribu...

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Bibliographic Details
Main Author: Muhammad Aslam
Format: Article
Language:English
Published: SpringerOpen 2023-12-01
Series:Journal of Big Data
Subjects:
Neutrosophic data
Classical statistics
Simulation
Weibull distribution
Indeterminacy
Online Access:https://doi.org/10.1186/s40537-023-00860-y

Internet

https://doi.org/10.1186/s40537-023-00860-y

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