Local sensitivity analysis for Bayesian inverse problems
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915910700236800 |
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| author | Dölz, Jürgen Ebert, David |
| author_facet | Dölz, Jürgen Ebert, David |
| contents | We present an extension of local sensitivity analysis, also referred to as the perturbation approach for uncertainty quantification, to Bayesian inverse problems. More precisely, we show how moments of random variables with respect to the posterior distribution can be approximated efficiently by asymptotic expansions. This is under the assumption that the measurement operators and prediction functions are sufficiently smooth and their corresponding stochastic moments with respect to the prior distribution exist. Numerical experiments are presented to the illustrate the theoretical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local sensitivity analysis for Bayesian inverse problems Dölz, Jürgen Ebert, David Numerical Analysis We present an extension of local sensitivity analysis, also referred to as the perturbation approach for uncertainty quantification, to Bayesian inverse problems. More precisely, we show how moments of random variables with respect to the posterior distribution can be approximated efficiently by asymptotic expansions. This is under the assumption that the measurement operators and prediction functions are sufficiently smooth and their corresponding stochastic moments with respect to the prior distribution exist. Numerical experiments are presented to the illustrate the theoretical results. |
| title | Local sensitivity analysis for Bayesian inverse problems |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2503.20526 |