Full metadata record
| DC Field | Value | Language | 
|---|---|---|
| dc.contributor.author | Shim, Jae Wan | - | 
| dc.date.accessioned | 2024-01-12T06:35:02Z | - | 
| dc.date.available | 2024-01-12T06:35:02Z | - | 
| dc.date.created | 2023-08-01 | - | 
| dc.date.issued | 2023-07 | - | 
| dc.identifier.issn | 0020-7748 | - | 
| dc.identifier.uri | https://pubs.kist.re.kr/handle/201004/79874 | - | 
| dc.description.abstract | We demonstrate that the most probable state of a conserved system with a limited number of entities or molecules is the state where non-Gaussian and non-chi-square distributions govern. We have conducted a thought experiment using a specific setup. We have verified the mathematical derivation of the most probable state accurately predicts the results obtained by computer simulations. The derived distributions approach the Gaussian and the chi-square distributions as the number of entities approaches infinity. | - | 
| dc.language | English | - | 
| dc.publisher | Springer | - | 
| dc.title | Generalized Entropy Approach for Conserved Systems with Finite Entities: Insights into Non-Gaussian and Non-Chi-Square Distributions using Havrda-Charvat-Tsallis Entropy | - | 
| dc.type | Article | - | 
| dc.identifier.doi | 10.1007/s10773-023-05426-5 | - | 
| dc.description.journalClass | 1 | - | 
| dc.identifier.bibliographicCitation | International Journal of Theoretical Physics, v.62, no.8 | - | 
| dc.citation.title | International Journal of Theoretical Physics | - | 
| dc.citation.volume | 62 | - | 
| dc.citation.number | 8 | - | 
| dc.description.isOpenAccess | N | - | 
| dc.description.journalRegisteredClass | scie | - | 
| dc.description.journalRegisteredClass | scopus | - | 
| dc.identifier.wosid | 001038999400003 | - | 
| dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - | 
| dc.relation.journalResearchArea | Physics | - | 
| dc.type.docType | Article | - | 
| dc.subject.keywordAuthor | Non-chi-square distribution | - | 
| dc.subject.keywordAuthor | Statistical modeling | - | 
| dc.subject.keywordAuthor | Non-Gaussian distribution | - | 
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