Bayesian Subspace Identification in the MIMO Case

TL;DR

This paper extends Bayesian Subspace System Identification to MIMO systems by deriving new priors and posteriors, validated through numerical experiments on the DAISY dataset, enhancing modeling capabilities for complex systems.

Contribution

The paper introduces novel equivariant priors and posterior distributions tailored for MIMO systems within the Bayesian subspace identification framework.

Findings

Successful extension of Bayesian subspace methods to MIMO systems

Validation of the approach with numerical results on the DAISY dataset

Improved modeling accuracy for MIMO system identification

Abstract

This report investigates the extension of the Bayesian Subspace System Identification method proposed in our previous work to the Multiple-Input Multiple-Output (MIMO) case. We derive new equivariant priors and posterior distributions specifically suited for the MIMO framework. Numerical results utilizing the DAISY dataset are reported to validate the approach.