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Temporal Parallelization of Bayesian Smoothers
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DOI：10.1109/TAC.2020.2976316.png)
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This article 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.
Keyword:
Bayes methods
Smoothing methods
Mathematical model
Computational modeling
Kalman filters
Parallel algorithms
Bayesian smoothing
Kalman filtering and smoothing
parallel computing
parallel scan
prefix sums
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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