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Robust expected information gain for optimal Bayesian experimental design using ambiguity sets.

, and . UAI, volume 180 of Proceedings of Machine Learning Research, page 728-737. PMLR, (2022)

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Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet., , , and . J. Comput. Phys., (2015)Solution of Nonlinear Stokes Equations Discretized By High-Order Finite Elements on Nonconforming and Anisotropic Meshes, with Application to Ice Sheet Dynamics., , and . SIAM J. Sci. Comput., (2015)Multiphysics simulations, , , , , , , , , and 35 other author(s). The International Journal of High Performance Computing Applications, 27 (1): 4--83 (February 2013)Solution of nonlinear Stokes equations discretized by high-order finite elements on nonconforming and anisotropic meshes, with application to ice sheet dynamics., , and . CoRR, (2014)Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data., , , and . VECPAR, volume 10150 of Lecture Notes in Computer Science, page 3-6. Springer, (2016)Extreme-Scale AMR., , , , , , and . SC, page 1-12. IEEE, (2010)Robust expected information gain for optimal Bayesian experimental design using ambiguity sets., and . UAI, volume 180 of Proceedings of Machine Learning Research, page 728-737. PMLR, (2022)On the Number of Face-Connected Components of Morton-Type Space-Filling Curves., , and . Found. Comput. Math., 19 (4): 843-868 (2019)Recursive Algorithms for Distributed Forests of Octrees., , , and . CoRR, (2014)Recursive, Parameter-Free, Explicitly Defined Interpolation Nodes for Simplices.. SIAM J. Sci. Comput., 42 (6): A4046-A4062 (2020)