A Sequential Sensor Selection Strategy for Hyper-Parameterized Linear Bayesian Inverse Problems
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Publication:5152842
DOI10.1007/978-3-030-55874-1_48zbMath1471.62435arXiv2011.11391OpenAlexW3165184084MaRDI QIDQ5152842
Peng Chen, Karen Veroy, Nicole Aretz-Nellesen, Martin A. Grepl
Publication date: 27 September 2021
Published in: Lecture Notes in Computational Science and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2011.11391
Optimal statistical designs (62K05) Bayesian inference (62F15) Numerical solution to inverse problems in abstract spaces (65J22)
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A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design ⋮ A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models ⋮ Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging
Cites Work
- On Bayesian A- and D-optimal experimental designs in infinite dimensions
- 3D-VAR for parameterized partial differential equations: a certified reduced basis approach
- An introduction to infinite-dimensional analysis
- Inverse problems: A Bayesian perspective
- A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification
- A parameterized-background data-weak approach to variational data assimilation: formulation, analysis, and application to acoustics
- Optimal Measurement Methods for Distributed Parameter System Identification
- Greedy Algorithms for Optimal Measurements Selection in State Estimation Using Reduced Models
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