An understandable way to discover methods to model interval input–output samples

This paper shows an application of plausible reasoning methods (mainly, specialization and analogous) in mathematical modeling. Our attention is how a practitioner to determine analogously a more balanced scientific model to assist the desire during solving the entire problem. Taking interval sample...

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Detalles Bibliográficos
Autores principales: Xu, Shan, Li, Shenggang, Liu, Heng, Garg, Harish, Jin, Xuqin, Zhao, Jingjing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312378/
http://dx.doi.org/10.1007/s40314-021-01561-z
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author Xu, Shan
Li, Shenggang
Liu, Heng
Garg, Harish
Jin, Xuqin
Zhao, Jingjing
author_facet Xu, Shan
Li, Shenggang
Liu, Heng
Garg, Harish
Jin, Xuqin
Zhao, Jingjing
author_sort Xu, Shan
collection PubMed
description This paper shows an application of plausible reasoning methods (mainly, specialization and analogous) in mathematical modeling. Our attention is how a practitioner to determine analogously a more balanced scientific model to assist the desire during solving the entire problem. Taking interval samples modeling as a problem, we exemplify (with consideration paid to the motivation and course of discovering) how to discover, based on the classical corresponding methods, three linear regression models and two linear-like interpolation models relying on n-variable interval input-1-variable interval output samples. The rationality of these recommended models are proved, and applications of them are illuminated in detail by examples. Strategies to model further interval samples towards satisfactory are also exposed.
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spelling pubmed-83123782021-07-26 An understandable way to discover methods to model interval input–output samples Xu, Shan Li, Shenggang Liu, Heng Garg, Harish Jin, Xuqin Zhao, Jingjing Comp. Appl. Math. Article This paper shows an application of plausible reasoning methods (mainly, specialization and analogous) in mathematical modeling. Our attention is how a practitioner to determine analogously a more balanced scientific model to assist the desire during solving the entire problem. Taking interval samples modeling as a problem, we exemplify (with consideration paid to the motivation and course of discovering) how to discover, based on the classical corresponding methods, three linear regression models and two linear-like interpolation models relying on n-variable interval input-1-variable interval output samples. The rationality of these recommended models are proved, and applications of them are illuminated in detail by examples. Strategies to model further interval samples towards satisfactory are also exposed. Springer International Publishing 2021-07-26 2021 /pmc/articles/PMC8312378/ http://dx.doi.org/10.1007/s40314-021-01561-z Text en © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Xu, Shan
Li, Shenggang
Liu, Heng
Garg, Harish
Jin, Xuqin
Zhao, Jingjing
An understandable way to discover methods to model interval input–output samples
title An understandable way to discover methods to model interval input–output samples
title_full An understandable way to discover methods to model interval input–output samples
title_fullStr An understandable way to discover methods to model interval input–output samples
title_full_unstemmed An understandable way to discover methods to model interval input–output samples
title_short An understandable way to discover methods to model interval input–output samples
title_sort understandable way to discover methods to model interval input–output samples
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312378/
http://dx.doi.org/10.1007/s40314-021-01561-z
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