The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
The double exponential WJ distribution has been shown to competently describe extreme events and critical phenomena, while the Gaussian function has celebrated rich applications in many other fields. Here we present the analysis that the WJ distribution may be properly treated as an extended Gaussia...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7773 |
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| author | Shurong Ge Junhua Wu |
| author_facet | Shurong Ge Junhua Wu |
| author_sort | Shurong Ge |
| collection | DOAJ |
| description | The double exponential WJ distribution has been shown to competently describe extreme events and critical phenomena, while the Gaussian function has celebrated rich applications in many other fields. Here we present the analysis that the WJ distribution may be properly treated as an extended Gaussian function. Based on the Taylor expansion, we propose three methods to formulate the WJ distribution in the form of Gaussian functions, with Method I and Method III being accurate and self-consistent, and elaborate the relationship among the parameters of the functions. Moreover, we derive the parameter scaling formula of the WJ distribution to express a general Gaussian function, with the work illustrated by a classical case of extreme events and critical phenomena and application to topical medical image processing to prove the effectiveness of the WJ distribution rather than the Gaussian function. Our results sturdily advocate that the WJ distribution can elegantly represent a Gaussian function of arbitrary parameters, whereas the latter usually is not able to satisfactorily describe the former except for specific parameter sets. Thus, it is conclusive that the WJ distribution offers applicability in extreme events and critical phenomena as well as processes describable by the Gaussian function, namely, implying plausibly a unifying approach to the pertinent data processing of those quite distinct areas and establishing a link between relevant extreme value theories and Gaussian processes. |
| first_indexed | 2024-03-09T12:47:28Z |
| format | Article |
| id | doaj.art-f2d42b4c268c430bba05786644af17d1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:47:28Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f2d42b4c268c430bba05786644af17d12023-11-30T22:11:14ZengMDPI AGApplied Sciences2076-34172022-08-011215777310.3390/app12157773The Analysis of WJ Distribution as an Extended Gaussian Function: Case StudyShurong Ge0Junhua Wu1Peter Grünberg Research Center, Nanjing University of Posts and Telecommunications, No. 66 Xinmofanmalu Road, Nanjing 210003, ChinaPeter Grünberg Research Center, Nanjing University of Posts and Telecommunications, No. 66 Xinmofanmalu Road, Nanjing 210003, ChinaThe double exponential WJ distribution has been shown to competently describe extreme events and critical phenomena, while the Gaussian function has celebrated rich applications in many other fields. Here we present the analysis that the WJ distribution may be properly treated as an extended Gaussian function. Based on the Taylor expansion, we propose three methods to formulate the WJ distribution in the form of Gaussian functions, with Method I and Method III being accurate and self-consistent, and elaborate the relationship among the parameters of the functions. Moreover, we derive the parameter scaling formula of the WJ distribution to express a general Gaussian function, with the work illustrated by a classical case of extreme events and critical phenomena and application to topical medical image processing to prove the effectiveness of the WJ distribution rather than the Gaussian function. Our results sturdily advocate that the WJ distribution can elegantly represent a Gaussian function of arbitrary parameters, whereas the latter usually is not able to satisfactorily describe the former except for specific parameter sets. Thus, it is conclusive that the WJ distribution offers applicability in extreme events and critical phenomena as well as processes describable by the Gaussian function, namely, implying plausibly a unifying approach to the pertinent data processing of those quite distinct areas and establishing a link between relevant extreme value theories and Gaussian processes.https://www.mdpi.com/2076-3417/12/15/7773distribution functionapplied physicsgaussian functionWJ distributionscalingcritical phenomena |
| spellingShingle | Shurong Ge Junhua Wu The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study Applied Sciences distribution function applied physics gaussian function WJ distribution scaling critical phenomena |
| title | The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study |
| title_full | The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study |
| title_fullStr | The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study |
| title_full_unstemmed | The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study |
| title_short | The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study |
| title_sort | analysis of wj distribution as an extended gaussian function case study |
| topic | distribution function applied physics gaussian function WJ distribution scaling critical phenomena |
| url | https://www.mdpi.com/2076-3417/12/15/7773 |
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