Generative Model Proposal based Particle Filtering for Data Assimilation
Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting, the goal is to model the posterior over the current state given all observations so far. Classical solutions typically make simplifying distributional or functional assumptions, e.g., linear-Gaussian systems, which can be inaccurate in many scenarios. In principle, particle filters (PFs) remove these assumptions, yet often collapse in high dimensions. Recent generative approaches learn conditional state transitions, but without principled Bayesian updates they do not recover the correct filtering posterior and can accumulate error over lon...
| Authors | Chandni Nagda, Mayank Shrivastava, Gudrun Thorkelsdottir et al. |
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| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 3 July 2026 |
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