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stat.ME2026
Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design
John D. Jakeman, Rebekah White, Bart van Bloemen Waanders +2
Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predi…
stat.ME2022
Hyper-differential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems
William Reese, Joseph Hart, Bart van Bloemen Waanders +3
Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain…