Guided filtering and smoothing for infinite-dimensional diffusions
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916946506678272 |
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| author | Pieper-Sethmacher, Thorben Avitabile, Daniele van der Meulen, Frank |
| author_facet | Pieper-Sethmacher, Thorben Avitabile, Daniele van der Meulen, Frank |
| contents | We consider the filtering and smoothing problems for an infinite-dimensional diffusion process X, observed through a finite-dimensional representation at discrete points in time. At the heart of our proposed methodology lies the construction of a path measure, termed the guided distribution of X, that is absolutely continuous with respect to the law of X, conditioned on the observations. We show that this distribution can be incorporated as a potent proposal measure for both sequential Monte Carlo as well as Markov Chain Monte Carlo schemes to tackle the filtering and smoothing problems respectively. In the offline setting, we extend our approach to incorporate parameter estimation of unknown model parameters. The proposed methodology is numerically illustrated in a case study for the stochastic Amari equation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guided filtering and smoothing for infinite-dimensional diffusions Pieper-Sethmacher, Thorben Avitabile, Daniele van der Meulen, Frank Probability We consider the filtering and smoothing problems for an infinite-dimensional diffusion process X, observed through a finite-dimensional representation at discrete points in time. At the heart of our proposed methodology lies the construction of a path measure, termed the guided distribution of X, that is absolutely continuous with respect to the law of X, conditioned on the observations. We show that this distribution can be incorporated as a potent proposal measure for both sequential Monte Carlo as well as Markov Chain Monte Carlo schemes to tackle the filtering and smoothing problems respectively. In the offline setting, we extend our approach to incorporate parameter estimation of unknown model parameters. The proposed methodology is numerically illustrated in a case study for the stochastic Amari equation. |
| title | Guided filtering and smoothing for infinite-dimensional diffusions |
| topic | Probability |
| url | https://arxiv.org/abs/2507.06786 |