3 papers
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.ME2026
A Bayesian Functional Concurrent Zero-Inflated Dirichlet-Multinomial Regression Model with Application to Infant Microbiome
Brody Erlandson, Ander Wilson, Matthew D. Koslovsky
The infant microbiome undergoes rapid changes in composition over time and is associated with long-term risks of conditions such as immune strength, allergy, asthma, and other heal…
stat.ME2025
A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data
Suppapat Korsurat, Matthew D. Koslovsky
Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals w…