113 citations · 294 across the 25 of their papers we have counts for
4 papers · 1 filter
Robust Inference and Model Criticism Using Bagged Posteriors
Jonathan H. Huggins, Jeffrey W. Miller
Standard Bayesian inference is known to be sensitive to model misspecification, leading to unreliable uncertainty quantification and poor predictive performance. However, finding g…
Validated Variational Inference via Practical Posterior Error Bounds
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell +1
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to th…
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal +1
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linea…
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Raj Agrawal, Jonathan H. Huggins, Brian Trippe +1
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian…