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 |
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