3 papers
stat.ME2025
Sparse Bayesian Partially Identified Models for Sequence Count Data
Won Gu, Francesca Chiaromonte, Justin D. Silverman
In genomics, differential abundance and expression analyses are complicated by the compositional nature of sequence count data, which reflect only relative-not absolute-abundances…
stat.AP2025
Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
Manan Saxena, Tinghua Chen, Justin D. Silverman
Many scientific fields collect longitudinal count compositional data. Each observation is a multivariate count vector, where the total counts are arbitrary, and the information lie…
stat.ME2025
Scalable Bayesian Semiparametric Additive Regression Models For Microbiome Studies
Tinghua Chen, Michelle Pistner Nixon, Justin D. Silverman
Statistical analysis of microbiome data is challenging. Bayesian multinomial logistic-normal (MLN) models have gained popularity due to their ability to account for the count compo…