Multi-index Sequential Monte Carlo Ratio Estimators for Bayesian Inverse problems.
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| Title: | Multi-index Sequential Monte Carlo Ratio Estimators for Bayesian Inverse problems. |
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| Authors: | Jasra, Ajay1, Law, Kody J. H.2, kody.law@manchester.ac.uk, Walton, Neil2, Yang, Shangda2 |
| Source: | Foundations of Computational Mathematics; Aug2024, Vol. 24 Issue 4, p1249-1304, 56p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 178913346 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10208-023-09612-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 56 StartPage: 1249 Titles: – TitleFull: Multi-index Sequential Monte Carlo Ratio Estimators for Bayesian Inverse problems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jasra, Ajay – PersonEntity: Name: NameFull: Law, Kody J. H. – PersonEntity: Name: NameFull: Walton, Neil – PersonEntity: Name: NameFull: Yang, Shangda IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 16153375 Numbering: – Type: volume Value: 24 – Type: issue Value: 4 Titles: – TitleFull: Foundations of Computational Mathematics Type: main |
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