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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.04145 |
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| _version_ | 1866909605687197696 |
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| author | Maio, Steven Alexanderian, Alen |
| author_facet | Maio, Steven Alexanderian, Alen |
| contents | We consider finite-dimensional linear Gaussian Bayesian inverse problems with uncorrelated sensor measurements. In this setting, it is known that the expected information gain, quantified by the expected Kullback-Leibler divergence from the posterior measure to the prior measure, is submodular. We present a simple alternative proof of this fact tailored to a weighted inner product space setting arising from discretization of infinite-dimensional inverse problems constrained by partial differential equations (PDEs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04145 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On submodularity of the expected information gain Maio, Steven Alexanderian, Alen Optimization and Control We consider finite-dimensional linear Gaussian Bayesian inverse problems with uncorrelated sensor measurements. In this setting, it is known that the expected information gain, quantified by the expected Kullback-Leibler divergence from the posterior measure to the prior measure, is submodular. We present a simple alternative proof of this fact tailored to a weighted inner product space setting arising from discretization of infinite-dimensional inverse problems constrained by partial differential equations (PDEs). |
| title | On submodularity of the expected information gain |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.04145 |