# On-line Bayesian System Identification

arXiv: https://arxiv.org/abs/1601.04251 · HTML: https://mlsanity.com/paper/1601.04251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty](https://mlsanity.com/paper/2203.00551/) (2022)
- [Adaptive Model Predictive Control by Learning Classifiers](https://mlsanity.com/paper/2203.06783/) (2022)

## Abstract (arXiv)

We consider an on-line system identification setting, in which new data become available at given time steps. In order to meet real-time estimation requirements, we propose a tailored Bayesian system identification procedure, in which the hyper-parameters are still updated through Marginal Likelihood maximization, but after only one iteration of a suitable iterative optimization algorithm. Both gradient methods and the EM algorithm are considered for the Marginal Likelihood optimization. We compare this "1-step" procedure with the standard one, in which the optimization method is run until convergence to a local minimum. The experiments we perform confirm the effectiveness of the approach we propose.