4 papers
Duality and Error for Predictively Oriented Inference
Aurya Javeed, Drew P. Kouri, Teresa Portone +1
Predictively oriented (PrO) inference quantifies uncertainty by selecting a distribution over model parameters to optimize a scoring rule applied to the induced predictive distribu…
Inference in the presence of model-form uncertainties: Leveraging a prediction-oriented approach to improve uncertainty characterization
Rebekah White, Rileigh Bandy, Teresa Portone
Bayesian inference is a popular approach to calibrating uncertainties, but it can underpredict such uncertainties when model misspecification is present, impacting its reliability…
Quantifying model prediction sensitivity to model-form uncertainty
Teresa Portone, Rebekah D. White, Joseph L. Hart
Model-form uncertainty (MFU) in assumptions made during physics-based model development is widely considered a significant source of uncertainty; however, there are limited approac…
Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion
Rebekah D. White, John D. Jakeman, Tim Wildey +1
Bayesian inference provides a powerful tool for leveraging observational data to inform model predictions and uncertainties. However, when such data is limited, Bayesian inference…