113 citations · 294 across the 25 of their papers we have counts for
5 papers · 1 filter
Calibrated Model Criticism Using Split Predictive Checks
Jiawei Li, Jonathan H. Huggins
Checking how well a fitted model explains the data is one of the most fundamental parts of a Bayesian data analysis. However, existing model checking methods suffer from trade-offs…
Independent finite approximations for Bayesian nonparametric inference
Tin D. Nguyen, Jonathan Huggins, Lorenzo Masoero +2
Completely random measures (CRMs) and their normalizations (NCRMs) offer flexible models in Bayesian nonparametrics. But their infinite dimensionality presents challenges for infer…
Reproducible Model Selection Using Bagged Posteriors
Jonathan H. Huggins, Jeffrey W. Miller
Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorre…
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…
Infinite Structured Hidden Semi-Markov Models
Jonathan H. Huggins, Frank Wood
This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models. We focus on variants of Bayesian…