Inference for Stationary Log-Gaussian Cox Point Processes using Bayesian Deep Learning: Application to Human Oral Microbiome Image Data.
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| Title: | Inference for Stationary Log-Gaussian Cox Point Processes using Bayesian Deep Learning: Application to Human Oral Microbiome Image Data. |
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| Authors: | Wang S; Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A., Wikle CK; Department of Statistics, University of Missouri-Columbia, Columbia, MO, U.S.A., Micheas AC; Department of Statistics, University of Missouri-Columbia, Columbia, MO, U.S.A., Welch JLM; The Forsyth Institute, Cambridge, MA, U.S.A., Starr JR; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, U.S.A.; Department of Medicine, Harvard Medical School, Boston, MA, U.S.A., Lee KH; Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A. |
| Source: | Spatial statistics [Spat Stat] 2026 Jun; Vol. 73. Date of Electronic Publication: 2026 Mar 21. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 101612400 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2211-6753 (Print) NLM ISO Abbreviation: Spat Stat Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 42226885 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Inference for Stationary Log-Gaussian Cox Point Processes using Bayesian Deep Learning: Application to Human Oral Microbiome Image Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Wang+S%22">Wang S</searchLink>; Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.<br /><searchLink fieldCode="AU" term="%22Wikle+CK%22">Wikle CK</searchLink>; Department of Statistics, University of Missouri-Columbia, Columbia, MO, U.S.A.<br /><searchLink fieldCode="AU" term="%22Micheas+AC%22">Micheas AC</searchLink>; Department of Statistics, University of Missouri-Columbia, Columbia, MO, U.S.A.<br /><searchLink fieldCode="AU" term="%22Welch+JLM%22">Welch JLM</searchLink>; The Forsyth Institute, Cambridge, MA, U.S.A.<br /><searchLink fieldCode="AU" term="%22Starr+JR%22">Starr JR</searchLink>; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, U.S.A.; Department of Medicine, Harvard Medical School, Boston, MA, U.S.A.<br /><searchLink fieldCode="AU" term="%22Lee+KH%22">Lee KH</searchLink>; Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101612400%22">Spatial statistics</searchLink> [Spat Stat] 2026 Jun; Vol. 73. <i>Date of Electronic Publication: </i>2026 Mar 21. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+B%2EV%22">Elsevier B.V </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>101612400 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Print <i>ISSN: </i>2211-6753 (Print) <i>NLM ISO Abbreviation: </i>Spat Stat <i>Subsets: </i>PubMed not MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=42226885 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.spasta.2026.100973 Languages: – Code: eng Text: English Titles: – TitleFull: Inference for Stationary Log-Gaussian Cox Point Processes using Bayesian Deep Learning: Application to Human Oral Microbiome Image Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang S – PersonEntity: Name: NameFull: Wikle CK – PersonEntity: Name: NameFull: Micheas AC – PersonEntity: Name: NameFull: Welch JLM – PersonEntity: Name: NameFull: Starr JR – PersonEntity: Name: NameFull: Lee KH IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: 2026 Jun Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2211-6753 Numbering: – Type: volume Value: 73 Titles: – TitleFull: Spatial statistics Type: main |
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