Academic Journal
Possibilistic Inferential Models: A Review.
| Title: | Possibilistic Inferential Models: A Review. |
|---|---|
| Authors: | Martin, Ryan1 (AUTHOR) rgmarti3@ncsu.edu |
| Source: | Journal of the American Statistical Association. Mar2026, Vol. 121 Issue 553, p807-826. 20p. |
| Subject Terms: | Possibility theory, Inferential statistics, Statistical models, Measurement uncertainty (Statistics), Resampling (Statistics) |
| Abstract: | An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher's fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn't require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfound connections with the imprecise probability literature, in particular, possibility theory. The brand of possibilistic IMs studied here are straightforward to construct, have very strong frequentist-like reliability properties, and offer fully conditional, Bayesian-like (imprecise) probabilistic reasoning. This article reviews these key recent developments, describing the new theory, methods, and computational tools. A generalization of the basic possibilistic IM is also presented, making new and unexpected connections with ideas in modern statistics and machine learning, for example, bootstrap and conformal prediction. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Business Source Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://www.jstor.org/openurl?issn=01621459&date=2026&volume=121&issue=553&spage=807 Name: JSTOR (all content) (s7799221) Category: fullText Text: Icon: https://imageserver.ebscohost.com/branding/jstor/iconJSTOR.gif MouseOverText: Full Text from JSTOR – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:bsx&genre=article&issn=01621459&ISBN=&volume=121&issue=553&date=20260301&spage=807&pages=807-826&title=Journal of the American Statistical Association&atitle=Possibilistic%20Inferential%20Models%3A%20A%20Review.&aulast=Martin%2C%20Ryan&id=DOI:10.1080/01621459.2025.2606127 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: bsx DbLabel: Business Source Index An: 193015013 RelevancyScore: 1390 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1390.35424804688 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Possibilistic Inferential Models: A Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Martin%2C+Ryan%22">Martin, Ryan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rgmarti3@ncsu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Statistical+Association%22">Journal of the American Statistical Association</searchLink>. Mar2026, Vol. 121 Issue 553, p807-826. 20p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Possibility+theory%22">Possibility theory</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+uncertainty+%28Statistics%29%22">Measurement uncertainty (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Resampling+%28Statistics%29%22">Resampling (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher's fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn't require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfound connections with the imprecise probability literature, in particular, possibility theory. The brand of possibilistic IMs studied here are straightforward to construct, have very strong frequentist-like reliability properties, and offer fully conditional, Bayesian-like (imprecise) probabilistic reasoning. This article reviews these key recent developments, describing the new theory, methods, and computational tools. A generalization of the basic possibilistic IM is also presented, making new and unexpected connections with ideas in modern statistics and machine learning, for example, bootstrap and conformal prediction. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=193015013 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/01621459.2025.2606127 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 807 Subjects: – SubjectFull: Possibility theory Type: general – SubjectFull: Inferential statistics Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Measurement uncertainty (Statistics) Type: general – SubjectFull: Resampling (Statistics) Type: general Titles: – TitleFull: Possibilistic Inferential Models: A Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Martin, Ryan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01621459 Numbering: – Type: volume Value: 121 – Type: issue Value: 553 Titles: – TitleFull: Journal of the American Statistical Association Type: main |
| ResultId | 1 |