Efficient Online Variational Estimation via Monte Carlo Sampling
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914310830161920 |
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| author | Chagneux, Mathis Müller, Mathias Gloaguen, Pierre Corff, Sylvain Le Olsson, Jimmy |
| author_facet | Chagneux, Mathis Müller, Mathias Gloaguen, Pierre Corff, Sylvain Le Olsson, Jimmy |
| contents | This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially. The algorithm allows for the simultaneous training of the model parameters and the distribution of the latent states given the observations. It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility. The performance of the method is illustrated on both synthetic data and real-world air-quality data. The proposed approach is theoretically motivated by the existence of an asymptotic contrast function and the ergodicity of the underlying Markov chain, and applies more generally to the computation of additive expectations under posterior distributions in state-space models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06579 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Online Variational Estimation via Monte Carlo Sampling Chagneux, Mathis Müller, Mathias Gloaguen, Pierre Corff, Sylvain Le Olsson, Jimmy Methodology Machine Learning This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially. The algorithm allows for the simultaneous training of the model parameters and the distribution of the latent states given the observations. It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility. The performance of the method is illustrated on both synthetic data and real-world air-quality data. The proposed approach is theoretically motivated by the existence of an asymptotic contrast function and the ergodicity of the underlying Markov chain, and applies more generally to the computation of additive expectations under posterior distributions in state-space models. |
| title | Efficient Online Variational Estimation via Monte Carlo Sampling |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2602.06579 |