Variational Kalman Filtering with Hinf-Based Correction for Robust Bayesian Learning in High Dimensions

  title={Variational Kalman Filtering with Hinf-Based Correction for Robust Bayesian Learning in High Dimensions},
  author={Niladri Das and Jed A. Duersch and Thomas A. Catanach},
— In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and H ∞ -norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian… 

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