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Variational Kalman Filtering with Hinf-Based Correction for Robust Bayesian Learning in High Dimensions
[article]
2022
arXiv
pre-print
In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-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
arXiv:2204.13089v1
fatcat:ttosagcdyff4nnuz4eq56ilzee