Regime Learning for Differentiable Particle Filters
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916530075205632 |
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| author | Brady, John-Joseph Luo, Yuhui Wang, Wenwu Elvira, Victor Li, Yunpeng |
| author_facet | Brady, John-Joseph Luo, Yuhui Wang, Wenwu Elvira, Victor Li, Yunpeng |
| contents | Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Regime Learning for Differentiable Particle Filters Brady, John-Joseph Luo, Yuhui Wang, Wenwu Elvira, Victor Li, Yunpeng Machine Learning Signal Processing 68T37 I.2.6 Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments. |
| title | Regime Learning for Differentiable Particle Filters |
| topic | Machine Learning Signal Processing 68T37 I.2.6 |
| url | https://arxiv.org/abs/2405.04865 |