Bayesian Parameter Identification for Jump Markov Linear Systems
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This paper presents a Bayesian method for identification of jump Markov linear systems that is powered by a Markov chain Monte Carlo method called the Gibbs sampler. Unlike maximum likelihood approaches, this method provides the parameter distributions or the variation of likely system responses, which could be useful for analysing the stability margins of control schemes. We also include numerically robust implementation details and examples demonstrating the effectiveness of the proposed algorithm.
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