Computation-Aware Kalman Filtering and Smoothing

• URL: http://arxiv.org/abs/2405.08971v1
• Date: Tue, 14 May 2024 21:31:11 GMT
• Title: Computation-Aware Kalman Filtering and Smoothing
• Authors: Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig,
• Abstract summary: We propose a probabilistic numerical inference for high-dimensional Gauss-ov models. Our algorithm leverages GPU acceleration and crucially enables a tunable trade-off between predictive cost and uncertainty.
• Score: 27.55456716194024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with the size of the state space. This is particularly problematic in spatiotemporal regression problems, where the state dimension scales with the number of spatial observations. Existing approximate frameworks leverage low-rank approximations of the covariance matrix. Since they do not model the error introduced by the computational approximation, their predictive uncertainty estimates can be overly optimistic. In this work, we propose a probabilistic numerical method for inference in high-dimensional Gauss-Markov models which mitigates these scaling issues. Our matrix-free iterative algorithm leverages GPU acceleration and crucially enables a tunable trade-off between computational cost and predictive uncertainty. Finally, we demonstrate the scalability of our method on a large-scale climate dataset.

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