Posterior exploration for computationally intensive forward models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913338200424448 |
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| author | Lykkegaard, Mikkel B. Fox, Colin Higdon, Dave Reese, C. Shane Moulton, J. David |
| author_facet | Lykkegaard, Mikkel B. Fox, Colin Higdon, Dave Reese, C. Shane Moulton, J. David |
| contents | In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site Metropolis updates. We show how fast, approximate models can be leveraged to improve the MCMC sampling efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Posterior exploration for computationally intensive forward models Lykkegaard, Mikkel B. Fox, Colin Higdon, Dave Reese, C. Shane Moulton, J. David Computation In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site Metropolis updates. We show how fast, approximate models can be leveraged to improve the MCMC sampling efficiency. |
| title | Posterior exploration for computationally intensive forward models |
| topic | Computation |
| url | https://arxiv.org/abs/2405.00397 |