Bayesian Parameter Identification for Jump Markov Linear Systems
Methodology
2021-02-11 v2 Systems and Control
Systems and Control
Applications
Abstract
This paper presents a Bayesian method for identification of jump Markov linear system parameters. A primary motivation is to provide accurate quantification of parameter uncertainty without relying on asymptotic in data-length arguments. To achieve this, the paper details a particle-Gibbs sampling approach that provides samples from the desired posterior distribution. These samples are produced by utilising a modified discrete particle filter and carefully chosen conjugate priors.
Cite
@article{arxiv.2004.08565,
title = {Bayesian Parameter Identification for Jump Markov Linear Systems},
author = {Mark P. Balenzuela and Adrian G. Wills and Christopher Renton and Brett Ninness},
journal= {arXiv preprint arXiv:2004.08565},
year = {2021}
}