Deep Variational Sequential Monte Carlo for High-Dimensional Observations
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913120537018368 |
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| author | van Nierop, Wessel L. Shlezinger, Nir van Sloun, Ruud J. G. |
| author_facet | van Nierop, Wessel L. Shlezinger, Nir van Sloun, Ruud J. G. |
| contents | Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Variational Sequential Monte Carlo for High-Dimensional Observations van Nierop, Wessel L. Shlezinger, Nir van Sloun, Ruud J. G. Machine Learning Signal Processing Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution. |
| title | Deep Variational Sequential Monte Carlo for High-Dimensional Observations |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2501.05982 |