# Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems

@article{Jacob2018BayesianII, title={Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems}, author={Pierre E. Jacob and Seyed Mohammad Mahdi Alavi and Adam Mahdi and Stephen John Payne and David A. Howey}, journal={IEEE Transactions on Control Systems Technology}, year={2018}, volume={26}, pages={497-506} }

Battery impedance spectroscopy models are given by fractional-order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is, therefore, challenging, especially for noncommensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods… Expand

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