A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems
Applications
2020-04-21 v1 Systems and Control
Systems and Control
Abstract
Jump Markov linear systems (JMLS) are a useful class which can be used to model processes which exhibit random changes in behavior during operation. This paper presents a numerically stable method for learning the parameters of jump Markov linear systems using the expectation-maximisation (EM) approach. The solution provided herein is a deterministic algorithm, and is not a Monte Carlo based technique. As a result, simulations show that when compared to alternative approaches, a more likely set of system parameters can be found within a fixed computation time, which better explain the observations of the system.
Cite
@article{arxiv.2004.08564,
title = {A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems},
author = {Mark P. Balenzuela and Adrian G. Wills and Christopher Renton and Brett Ninness},
journal= {arXiv preprint arXiv:2004.08564},
year = {2020}
}