Sparse Bayesian Nonlinear System Identification Using Variational Inference

@article{Jacobs2018SparseBN,
  title={Sparse Bayesian Nonlinear System Identification Using Variational Inference},
  author={William R. Jacobs and Tara Baldacchino and Tony J. Dodd and Sean R. Anderson},
  journal={IEEE Transactions on Automatic Control},
  year={2018},
  volume={63},
  pages={4172-4187}
}
Bayesian nonlinear system identification for one of the major classes of dynamic model, the nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied to date. Markov chain Monte Carlo (MCMC) methods have been developed, which tend to be accurate but can also be slow to converge. In this contribution, we present a novel, computationally efficient solution to sparse Bayesian identification of the NARX model using variational inference, which is orders of magnitude… CONTINUE READING

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