5 papers · 1 filter
Uncertainty Quantification in Bayesian Clustering
Garritt L. Page, Andrés F. Barrientos, David B. Dahl +1
Bayesian clustering methods have the widely touted advantage of providing a probabilistic characterization of uncertainty in clustering through the posterior distribution. An amazi…
Targeted empirical Bayes for more supervised joint factor analysis
Glenn Palmer, David B. Dunson
Joint Bayesian factor models are popular for characterizing relationships between multivariate correlated predictors and a response variable. Standard models assume that all variab…
Scalable and robust regression models for continuous proportional data
Changwoo J. Lee, Benjamin K. Dahl, Otso Ovaskainen +1
Beta regression is used routinely for continuous proportional data, but it often encounters practical issues such as a lack of robustness to misspecification of the beta distributi…
Nested exemplar latent space models for dimension reduction in dynamic networks
Jennifer Noelle Kampe, Luca Alessandro Silva, Tomas Roslin +1
Dynamic latent space models are widely used for characterizing changes in networks and relational data over time. These models assign to each node latent attributes that characteri…
Marginally interpretable spatial logistic regression with bridge processes
Changwoo J. Lee, David B. Dunson
In including random effects to account for dependent observations, the odds ratio interpretation of logistic regression coefficients is changed from population-averaged to subject-…