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A function dataset for benchmarking in sensitivity analysis
In this paper a dataset of functions has been described which includes analytical values of Sobol’ first-order and total-order indices. This unique collection represents a valid benchmark to evaluate sensitivity analysis methodologies and allows the comparison of different technique outcomes. The be...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989694/ https://www.ncbi.nlm.nih.gov/pubmed/35402667 http://dx.doi.org/10.1016/j.dib.2022.108071 |
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author | Azzini, Ivano Rosati, Rossana |
author_facet | Azzini, Ivano Rosati, Rossana |
author_sort | Azzini, Ivano |
collection | PubMed |
description | In this paper a dataset of functions has been described which includes analytical values of Sobol’ first-order and total-order indices. This unique collection represents a valid benchmark to evaluate sensitivity analysis methodologies and allows the comparison of different technique outcomes. The benchmarking dataset was introduced in Azzini and Rosati following a practice already consolidated in many fields of research such as machine learning or Statistics. The dataset should be considered as an initial proposal open to being easily updated, extended, or modified by new mathematical functions or models in the future. |
format | Online Article Text |
id | pubmed-8989694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-89896942022-04-09 A function dataset for benchmarking in sensitivity analysis Azzini, Ivano Rosati, Rossana Data Brief Data Article In this paper a dataset of functions has been described which includes analytical values of Sobol’ first-order and total-order indices. This unique collection represents a valid benchmark to evaluate sensitivity analysis methodologies and allows the comparison of different technique outcomes. The benchmarking dataset was introduced in Azzini and Rosati following a practice already consolidated in many fields of research such as machine learning or Statistics. The dataset should be considered as an initial proposal open to being easily updated, extended, or modified by new mathematical functions or models in the future. Elsevier 2022-03-20 /pmc/articles/PMC8989694/ /pubmed/35402667 http://dx.doi.org/10.1016/j.dib.2022.108071 Text en © 2022 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Azzini, Ivano Rosati, Rossana A function dataset for benchmarking in sensitivity analysis |
title | A function dataset for benchmarking in sensitivity analysis |
title_full | A function dataset for benchmarking in sensitivity analysis |
title_fullStr | A function dataset for benchmarking in sensitivity analysis |
title_full_unstemmed | A function dataset for benchmarking in sensitivity analysis |
title_short | A function dataset for benchmarking in sensitivity analysis |
title_sort | function dataset for benchmarking in sensitivity analysis |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989694/ https://www.ncbi.nlm.nih.gov/pubmed/35402667 http://dx.doi.org/10.1016/j.dib.2022.108071 |
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