Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes

The paper introduces MEGPODE, a Bayesian nonparametric mixed‑effect ODE framework that models each subject’s dynamics as a combination of a shared population component and a subject‑specific deviation, both governed by Gaussian process priors. It avoids repeated ODE solves by representing latent trajectories with state‑space GP priors and penalizing derivative discrepancies on a finite time grid, enabling a single variational objective for joint inference of trajectories and vector fields. Experiments on heterogeneous oscillatory and biomedical ODE benchmarks show that MEGPODE outperforms strong baselines in recovering population‑level fields and predicting subject‑level trajectories.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• benchmarks