OdeMMHPlanner.jl

Initial public release of OdeMMHPlanner. This release provides: Bayesian inference of unknown dynamics and latent state trajectories from infrequent output measurements A Marginal Metropolis–Hastings sampler with ODE-integrated latent trajectories Scenario-based optimal control under explicit model uncertainty Reproducible experiment scripts accompanying the paper This release corresponds to the experiments reported in: "Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control".