Distributional sensitivity analysis
Among the uses for global sensitivity analysis is factor prioritization. A key assumption for this is that a given factor can, through further research, be fixed to some point on its domain. For factors containing epistemic uncertainty, this is an optimistic assumption, which can lead to inappropria...
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| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/96179 https://orcid.org/0000-0003-2156-9338 |
| _version_ | 1826215237038112768 |
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| author | Allaire, Douglas L. Willcox, Karen E. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Allaire, Douglas L. Willcox, Karen E. |
| author_sort | Allaire, Douglas L. |
| collection | MIT |
| description | Among the uses for global sensitivity analysis is factor prioritization. A key assumption for this is that a given factor can, through further research, be fixed to some point on its domain. For factors containing epistemic uncertainty, this is an optimistic assumption, which can lead to inappropriate resource allocation. Thus, this research develops an original method, referred to as distributional sensitivity analysis, that considers which factors would on average cause the greatest reduction in output variance, given that the portion of a particular factor's variance that can be reduced is a random variable. A key aspect of the method is that the analysis is performed directly on the samples that were generated during a global sensitivity analysis using acceptance/rejection sampling. In general, if for each factor, N model runs are required for a global sensitivity analysis, then those same N model runs are sufficient for a distributional sensitivity analysis. |
| first_indexed | 2024-09-23T16:19:43Z |
| format | Article |
| id | mit-1721.1/96179 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:19:43Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/961792022-10-02T07:43:10Z Distributional sensitivity analysis Allaire, Douglas L. Willcox, Karen E. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Allaire, Douglas L. Willcox, Karen E. Among the uses for global sensitivity analysis is factor prioritization. A key assumption for this is that a given factor can, through further research, be fixed to some point on its domain. For factors containing epistemic uncertainty, this is an optimistic assumption, which can lead to inappropriate resource allocation. Thus, this research develops an original method, referred to as distributional sensitivity analysis, that considers which factors would on average cause the greatest reduction in output variance, given that the portion of a particular factor's variance that can be reduced is a random variable. A key aspect of the method is that the analysis is performed directly on the samples that were generated during a global sensitivity analysis using acceptance/rejection sampling. In general, if for each factor, N model runs are required for a global sensitivity analysis, then those same N model runs are sufficient for a distributional sensitivity analysis. 2015-03-25T16:28:43Z 2015-03-25T16:28:43Z 2010-06 Article http://purl.org/eprint/type/JournalArticle 18770428 http://hdl.handle.net/1721.1/96179 Allaire, Douglas L., and Karen E. Willcox. “Distributional Sensitivity Analysis.” Procedia - Social and Behavioral Sciences 2, no. 6 (2010): 7595–7596. https://orcid.org/0000-0003-2156-9338 en_US http://dx.doi.org/10.1016/j.sbspro.2010.05.134 Procedia - Social and Behavioral Sciences Creative Commons Attribution http://creativecommons.org/licenses/by-nc-nd/3.0/ application/pdf Elsevier Elsevier |
| spellingShingle | Allaire, Douglas L. Willcox, Karen E. Distributional sensitivity analysis |
| title | Distributional sensitivity analysis |
| title_full | Distributional sensitivity analysis |
| title_fullStr | Distributional sensitivity analysis |
| title_full_unstemmed | Distributional sensitivity analysis |
| title_short | Distributional sensitivity analysis |
| title_sort | distributional sensitivity analysis |
| url | http://hdl.handle.net/1721.1/96179 https://orcid.org/0000-0003-2156-9338 |
| work_keys_str_mv | AT allairedouglasl distributionalsensitivityanalysis AT willcoxkarene distributionalsensitivityanalysis |