4 papers · 1 filter
An Unscented PrO Kalman Filter
Dan Kean, Zheyang Shen, Chris. J. Oates
Predictively-oriented (PrO) filtering for state-space models was proposed in Shen et al. [2026] as a promising route to avoiding over-confident inferences in settings where either…
Predictively-Oriented Kalman Filtering
Zheyang Shen, Gerardo Duran-Martin, Chris. J. Oates
This paper presents a post-Bayesian approach to online filtering in nonlinear state-space models, capable of avoiding over-confident inferences in settings where either the dynamic…
Detecting Model Misspecification in Bayesian Inverse Problems via Variational Gradient Descent
Qingyang Liu, Matthew A. Fisher, Zheyang Shen +4
Bayesian inference is optimal when the statistical model is well-specified, while outside this setting Bayesian inference can catastrophically fail; accordingly a wealth of post-Ba…
Prediction-Centric Uncertainty Quantification via MMD
Zheyang Shen, Jeremias Knoblauch, Sam Power +1
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily s…