2 citations · 2 across the 4 of their papers we have counts for
7 papers · 1 filter
-PSD: Scalable Approximate SNR-Optimised Polynomial Stein Discrepancies
Minh-Long Nguyen, Thanh-Long Vu, Christopher Drovandi +2
Polynomial Stein discrepancies (PSD) provide a scalable alternative to kernel Stein methods for measuring sample quality and goodness-of-fit testing, but their statistical properti…
Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
Arwen Nugteren, Mahdi Abolghasemi, Kerrie Mengersen +1
Decision-making in hierarchical systems requires probabilistic forecasts at all cross-sectional levels. Current hierarchical forecasting methods typically generate independent fore…
Bayesian inference for ordinary differential equations models with heteroscedastic measurement error
Selva Salimi, David J. Warne, Christopher Drovandi
Ordinary differential equation (ODE) models are widely used to describe systems in many areas of science. To ensure these models provide accurate and interpretable representations…
Calibrated Generalized Bayesian Inference
David T. Frazier, Christopher Drovandi, Robert Kohn
We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors.…
A Principled Approach to Bayesian Transfer Learning
Adam Bretherton, Joshua J. Bon, David J. Warne +2
Updating information given some observed data is the core tenet of Bayesian inference. Bayesian transfer learning extends this idea by incorporating information…
Robustifying Approximate Bayesian Computation
Chaya Weerasinghe, David T. Frazier, Ruben Loaiza-Maya +1
Approximate Bayesian computation (ABC) is one of the most popular "likelihood-free" methods. These methods have been applied in a wide range of fields by providing solutions to int…