Modern methods for Bayesian regression beyond the Gaussian response setting are often
computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent …

Gaussian approximations are routinely employed in Bayesian statistics to ease inference
when the target posterior is intractable. Although these approximations are asymptotically …

Computationally efficient Bayesian unit-level models for non-Gaussian data under
informative sampling with application to estima Page 1 The Annals of Applied Statistics 2022 …

Abstract Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been
successfully applied to many different scientific areas and is particularly well suited to …

There is a growing interest in learning how the distribution of a response variable changes
with a set of observed predictors. Bayesian nonparametric dependent mixture models …

Spatial count data models are used to explain and predict the frequency of phenomena such
as traffic accidents in geographically distinct entities such as census tracts or road segments …

Factors models are commonly used to analyze high-dimensional data in both single-study
and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte …

Gaussian distributions are widely used in Bayesian variational inference to approximate
intractable posterior densities, but the ability to accommodate skewness can improve …

We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical
data. GLMs have been difficult to scale to more than a few dozen categories due to non …

We consider a latent space model for dynamic networks, where our objective is to estimate
the pairwise inner products of the latent positions. To balance posterior inference and …