13 citations · 39 across the 28 of their papers we have counts for
30 papers
Bayesian inference on beta diversity via feature allocation models
Federica Stolf, Tommaso Rigon, David B. Dunson
Beta diversity quantifies variation in species composition across ecological communities and is fundamental for understanding biodiversity patterns across space and environmental g…
Overfitted high-dimensional matrix factorizations via adaptive spectral shrinkage
Lorenzo Mauri, David B. Dunson
Factor models are popular approaches for analyzing high-dimensional data to extract low-rank signals and estimate covariances. They decompose the covariance matrix as the sum of lo…
Pathway-based Bayesian factor models for 'omics data
Lorenzo Mauri, Federica Stolf, Amy H. Herring +2
Interpreting RNA-sequencing data requires identifying coordinated gene expression patterns that correspond to biological pathways. Standard factor models provide useful dimension r…
Model-based indicators for co-clustered environments and species communities
Braden Scherting, Otso Ovaskainen, Tomas Roslin +1
Accurate biodiversity monitoring is essential for effective environmental policy, yet current practices often rely on arbitrarily defined ecosystems, communities, and ad-hoc indica…
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…
Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
Alexander Dombowsky, David B. Dunson
A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling include classification and nonparametric…
Inference on covariance structure in high-dimensional multi-view data
Lorenzo Mauri, David B. Dunson
This article focuses on covariance estimation for multi-view data. Popular approaches rely on factor-analytic decompositions that have shared and view-specific latent factors. Post…
A note on simulation methods for the Dirichlet-Laplace prior
Luis Gruber, Gregor Kastner, Anirban Bhattacharya +3
Bhattacharya et al. (2015, Journal of the American Statistical Association 110(512): 1479-1490) introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chai…
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…
Feature aware covariance estimation, with application to mixtures of chemical exposures
Elizabeth Bersson, Kate Hoffman, Heather M. Stapleton +1
The motivation of this article is to improve inferences on the covariation in environmental exposures, motivated by data from a study of Toddlers Exposure to SVOCs in Indoor Enviro…
Bayesian Latent Class Regression with Interpretable Binary Profiles
Yuren Zhou, Yuqi Gu, David B. Dunson
High-dimensional categorical data arise in diverse scientific domains and are often accompanied by covariates. Latent class regression models are routinely used in such settings, r…
Quantifying sleep apnea heterogeneity using hierarchical Bayesian modeling
Glenn Palmer, Narat Srivali, David B. Dunson
Obstructive Sleep Apnea (OSA) is a breathing disorder during sleep that affects millions of people worldwide. The diagnosis of OSA often occurs through an overnight polysomnogram (…
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…
Joint species distribution modeling of abundance data through latent variable barcodes
Braden Scherting, Otso Ovaskainen, David B. Dunson
Accelerating global biodiversity loss has highlighted the role of complex relationships and shared patterns among species in determining their responses to environmental changes. T…
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 Joint Additive Factor Models for Multiview Learning
Niccolo Anceschi, Federico Ferrari, David B. Dunson +1
It is increasingly common to collect data of multiple different types on the same set of samples. Our focus is on studying relationships between such multiview features and respons…
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 Learning of Clinically Meaningful Sepsis Phenotypes in Northern Tanzania
Alexander Dombowsky, David B. Dunson, Deng B. Madut +2
Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum…
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…
Product Centered Dirichlet Processes for Bayesian Multiview Clustering
Alexander Dombowsky, David B. Dunson
While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistica…