Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
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Computer Science > Machine Learning
Title:Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
Abstract:Estimating parameters of dynamical systems from sparse, noisy, and irregularly sampled data is often severely ill-conditioned. When multiple related datasets are available, they provide additional information if the shared structure and variability are properly modeled. We propose a hierarchical Bayesian framework for probabilistic meta-learning in dynamical systems, modeling dataset-specific parameters as draws from a shared population distribution. A numerical ODE solver is embedded within gradient-based MCMC to enable efficient posterior inference of the shared population and dataset-specific parameter distribution. Experiments show improved predictive performance over unpooled methods, highlighting the potential for data-efficient system identification in settings with sparse data.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.24966 [cs.LG] |
| (or arXiv:2606.24966v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24966
arXiv-issued DOI via DataCite
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