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arXiv · 2004.08564

A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems

Mark P. Balenzuela·Adrian G. Wills·Christopher Renton·Brett Ninness
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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.

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Mark P. Balenzuela, Adrian G. Wills, Christopher Renton, Brett Ninness. 2020-04-18. A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems. https://arxiv.org/abs/2004.08564

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