13 citations · 39 across the 28 of their papers we have counts for
7 papers · 2 filters
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-…
Bayesian modeling of multi-species labeling errors in ecological studies
Haoxuan Wang, Patrik Lauha, David B. Dunson
Ecological and conservation studies monitoring bird communities typically rely on species classification based on bird vocalizations. Historically, this has been based on expert vo…
Bayesian Deep Generative Models for Multiplex Networks with Multiscale Overlapping Clusters
Yuren Zhou, Yuqi Gu, David B. Dunson
Our interest is in multiplex network data with multiple network samples observed across the same set of nodes. Examples originate from a variety of fields, including brain connecti…
Bayesian Level Set Clustering
David Buch, Miheer Dewaskar, David B. Dunson
Classically, Bayesian clustering interprets each component of a mixture model as a cluster. The inferred clustering posterior is highly sensitive to any inaccuracies in the kernel…
Infinite joint species distribution models
Federica Stolf, David B. Dunson
Joint species distribution models are popular in ecology for modeling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multiv…
Logistic-beta processes for dependent random probabilities with beta marginals
Changwoo J. Lee, Alessandro Zito, Huiyan Sang +1
The beta distribution serves as a canonical tool for modeling probabilities in statistics and machine learning. However, there is limited work on flexible and computationally conve…