asynmintervals：一个Python库，用于不对称区间数的不确定性建模

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-09-30 DOI:10.1016/j.softx.2025.102380

Wojciech Sałabun

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引用次数: 0

摘要

摩尔区间算法使用对称边界表示不确定性，然而许多现实世界的量和操作表现出不对称行为。非对称区间数（ain）通过在边界内引入期望值并允许其两侧的概率密度不同来推广摩尔区间。这个概念相对较新，并且没有广泛可用的软件支持使用ai。本文通过提供一个开源Python库来解决这一问题，该库为ai提供了完整的算法、概率密度和累积分布的函数、分位数评估以及直观的可视化和汇总工具。基于NumPy和Matplotlib，它允许工程师和研究人员以最小的努力将不对称不确定性纳入模型和计算中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AsymIntervals: A Python library for uncertainty modeling with asymmetric interval numbers

Moore interval arithmetic represents uncertainty using symmetric bounds, yet many real-world quantities and operations exhibit asymmetric behavior. Asymmetric Interval Numbers (AINs) generalize Moore intervals by introducing an expected value within the bounds and allowing the probability density to differ on either side of it. This concept is relatively new, and no widely available software has supported use of AINs. This paper addresses this gap by presenting an open-source Python library that provides complete arithmetic for AINs, functions for probability density and cumulative distribution, quantile evaluation, and straightforward tools for visualization and summary. Built on NumPy and Matplotlib, it allows engineers and researchers to incorporate asymmetric uncertainty into models and calculations with minimal effort.

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.