Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866929485254754304 |
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| author | Breaz, Valentin Wilkinson, Richard |
| author_facet | Breaz, Valentin Wilkinson, Richard |
| contents | Posterior sampling for high-dimensional Bayesian inverse problems is a common challenge in real-world applications. Randomized Maximum Likelihood (RML) is an optimization based methodology that gives samples from an approximation to the posterior distribution. We develop a high-dimensional Bayesian Optimization (BO) approach based on Gaussian Process (GP) surrogate models to solve the RML problem. We demonstrate the benefits of our approach in comparison to alternative optimization methods on a variety of synthetic and real-world Bayesian inverse problems, including medical and magnetohydrodynamics applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_08022 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization Breaz, Valentin Wilkinson, Richard Computation Posterior sampling for high-dimensional Bayesian inverse problems is a common challenge in real-world applications. Randomized Maximum Likelihood (RML) is an optimization based methodology that gives samples from an approximation to the posterior distribution. We develop a high-dimensional Bayesian Optimization (BO) approach based on Gaussian Process (GP) surrogate models to solve the RML problem. We demonstrate the benefits of our approach in comparison to alternative optimization methods on a variety of synthetic and real-world Bayesian inverse problems, including medical and magnetohydrodynamics applications. |
| title | Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization |
| topic | Computation |
| url | https://arxiv.org/abs/2204.08022 |