Use of the Kalman filter for inference in state-space models with unknown noise distributions

@article{Maryak2004UseOT,
  title={Use of the Kalman filter for inference in state-space models with unknown noise distributions},
  author={J. L. Maryak and J. Spall and Bryan D. Heydon},
  journal={IEEE Transactions on Automatic Control},
  year={2004},
  volume={49},
  pages={87-90}
}
  • J. L. Maryak, J. Spall, Bryan D. Heydon
  • Published 2004
  • Mathematics, Computer Science
  • IEEE Transactions on Automatic Control
  • The Kalman filter is frequently used for state estimation in state-space models when the standard Gaussian noise assumption does not apply. A problem arises, however, in that inference based on the incorrect Gaussian assumption can lead to misleading or erroneous conclusions about the relationship of the Kalman filter estimate to the true (unknown) state. This note shows how inequalities from probability theory associated with the probabilities of convex sets have potential for characterizing… CONTINUE READING

    Figures and Topics from this paper.

    Explore Further: Topics Discussed in This Paper

    Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
    • 91
    • PDF
    Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures
    • 40
    • PDF
    A Modified Kalman Filter for Non-gaussian Measurement Noise
    • 5
    Worst-Case Prediction Performance Analysis of the Kalman Filter
    • 4
    • PDF
    Robust Filtering Through Coherent Lower Previsions
    • 36
    • Highly Influenced
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 32 REFERENCES
    Evaluation of convergence rate in the central limit theorem for the Kalman filter
    • 26
    On Gibbs sampling for state space models
    • 1,920