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arXiv · 1905.13002

Temporal Parallelization of Bayesian Smoothers

Simo Särkkä·Ángel F. García-Fernández
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Abstract

This paper presents algorithms for temporal parallelization of Bayesian smoothers. We define the elements and the operators to pose these problems as the solutions to all-prefix-sums operations for which efficient parallel scan-algorithms are available. We present the temporal parallelization of the general Bayesian filtering and smoothing equations and specialize them to linear/Gaussian models. The advantage of the proposed algorithms is that they reduce the linear complexity of standard smoothing algorithms with respect to time to logarithmic.

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Simo Särkkä, Ángel F. García-Fernández. 2019-05-30. Temporal Parallelization of Bayesian Smoothers. https://arxiv.org/abs/1905.13002

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