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stat.ME2026
A Comprehensive Bayesian Approach to Entity Resolution for Data with Multiple Truths
Hyungjoon Kim, Andee Kaplan, Matthew D. Koslovsky
In many applications, from government to ecology, integrating data from diverse and noisy sources is critical for downstream inference. However, a unique identifier to link records…
stat.ME2025
A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans
L. Drew, A. Kaplan, I. Breckheimer
In the information age, it has become increasingly common for data containing records about overlapping individuals to be distributed across multiple sources, making it necessary t…
stat.ME2024
A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data
Matthew D. Koslovsky, Andee Kaplan, Victoria A. Terranova +1
Measurement error in multinomial data is a well-known and well-studied inferential problem that is encountered in many fields, including engineering, biomedical and omics research,…