Distributions and Direct Parametrization for Stable Stochastic State-Space Models
Methodology
2025-09-23 v2 Systems and Control
Systems and Control
Abstract
We present a direct parametrization for continuous-time stochastic state-space models that ensures external stability via the stochastic bounded-real lemma. Our formulation facilitates the construction of probabilistic priors that enforce almost-sure stability, which are suitable for sampling-based Bayesian inference methods. We validate our work with a simulation example and demonstrate its ability to yield stable predictions with uncertainty quantification.
Cite
@article{arxiv.2503.14177,
title = {Distributions and Direct Parametrization for Stable Stochastic State-Space Models},
author = {Mohamad Al Ahdab and Zheng-Hua Tan and John Leth},
journal= {arXiv preprint arXiv:2503.14177},
year = {2025}
}