4 citations · 10 across the 16 of their papers we have counts for
8 papers · 1 filter
Leveraging ontologies to predict biological activity of chemicals across genes
Jennifer N. Kampe, David B. Dunson, Celeste K. Carberry +3
High-throughput screening (HTS) is useful for evaluating chemicals for potential human health risks. However, given the extraordinarily large number of genes, assay endpoints, and…
On the Posterior Computation Under the Dirichlet-Laplace Prior
Paolo Onorati, David B. Dunson, Antonio Canale
Modern applications routinely collect high-dimensional data, leading to statistical models having more parameters than there are samples available. A common solution is to impose s…
Local graph estimation with pathwise false discovery control
Omar Melikechi, David B. Dunson, Noureddine Melikechi +1
Many datasets include a small set of variables, such as biomarkers or clinical outcomes, whose relationships to the broader system are of primary scientific interest. Estimating th…
Nonparametric predictive inference for discrete data via Metropolis-adjusted Dirichlet sequences
Davide Agnoletto, Tommaso Rigon, David B. Dunson
This article is motivated by challenges in conducting Bayesian inferences on unknown discrete distributions, with a particular focus on count data. To avoid the computational disad…
Link prediction in ecological networks under extreme taxonomic bias
Jennifer N. Kampe, Camille M. M. DeSisto, David B. Dunson
Ecological networks offer powerful insights into community function, but without first characterizing these networks accurately, our ability to detect and interpret changes under e…
A Bayesian theory for estimation of biodiversity
Tommaso Rigon, Ching-Lung Hsu, David B. Dunson
Statistical inference on biodiversity has a rich history going back to RA Fisher. An influential ecological theory suggests the existence of a fundamental biodiversity number, deno…
Spectral decomposition-assisted multi-study factor analysis
Lorenzo Mauri, Niccolò Anceschi, David B. Dunson
This article focuses on covariance estimation for multi-study data. Popular approaches employ factor-analytic terms with shared and study-specific loadings that decompose the varia…
On the Statistical Capacity of Deep Generative Models
Edric Tam, David B. Dunson
Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not…