Affine invariant interacting Langevin dynamics in Markov chain importance sampling for rare event estimation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915358276845568 |
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| author | Beh, Jason Morio, Jérôme Simatos, Florian Weissmann, Simon |
| author_facet | Beh, Jason Morio, Jérôme Simatos, Florian Weissmann, Simon |
| contents | This work considers the framework of Markov chain importance sampling~(MCIS), in which one employs a Markov chain Monte Carlo~(MCMC) scheme to sample particles approaching the optimal distribution for importance sampling, prior to estimating the quantity of interest through importance sampling. In rare event estimation, the optimal distribution admits a non-differentiable log-density, thus gradient-based MCMC can only target a smooth approximation of the optimal density. We propose a new gradient-based MCIS scheme for rare event estimation, called affine invariant interacting Langevin dynamics for importance sampling~(ALDI-IS), in which the affine invariant interacting Langevin dynamics~(ALDI) is used to sample particles according to the smoothed zero-variance density. We establish a non-asymptotic error bound when importance sampling is used in conjunction with samples independently and identically distributed according to the smoothed optiaml density to estimate a rare event probability, and an error bound on the sampling bias when a simplified version of ALDI, the unadjusted Langevin algorithm, is used to sample from the smoothed optimal density. We show that the smoothing parameter of the optimal density has a strong influence and exhibits a trade-off between a low importance sampling error and the ease of sampling using ALDI. We perform a numerical study of ALDI-IS and illustrate this trade-off phenomenon on standard rare event estimation test cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Affine invariant interacting Langevin dynamics in Markov chain importance sampling for rare event estimation Beh, Jason Morio, Jérôme Simatos, Florian Weissmann, Simon Statistics Theory This work considers the framework of Markov chain importance sampling~(MCIS), in which one employs a Markov chain Monte Carlo~(MCMC) scheme to sample particles approaching the optimal distribution for importance sampling, prior to estimating the quantity of interest through importance sampling. In rare event estimation, the optimal distribution admits a non-differentiable log-density, thus gradient-based MCMC can only target a smooth approximation of the optimal density. We propose a new gradient-based MCIS scheme for rare event estimation, called affine invariant interacting Langevin dynamics for importance sampling~(ALDI-IS), in which the affine invariant interacting Langevin dynamics~(ALDI) is used to sample particles according to the smoothed zero-variance density. We establish a non-asymptotic error bound when importance sampling is used in conjunction with samples independently and identically distributed according to the smoothed optiaml density to estimate a rare event probability, and an error bound on the sampling bias when a simplified version of ALDI, the unadjusted Langevin algorithm, is used to sample from the smoothed optimal density. We show that the smoothing parameter of the optimal density has a strong influence and exhibits a trade-off between a low importance sampling error and the ease of sampling using ALDI. We perform a numerical study of ALDI-IS and illustrate this trade-off phenomenon on standard rare event estimation test cases. |
| title | Affine invariant interacting Langevin dynamics in Markov chain importance sampling for rare event estimation |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2506.20185 |