# Temporal Parallelization of Bayesian Smoothers

arXiv: https://arxiv.org/abs/1905.13002 · HTML: https://mlsanity.com/paper/1905.13002/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 30 of 33)

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- [Unbiased Learning of Deep Generative Models with Structured Discrete Representations](https://mlsanity.com/paper/2306.08230/) (2023)
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- [Context-Selective State Space Models: Feedback is All You Need](https://mlsanity.com/paper/2510.14027/) (2025)
- [Temporal Gaussian Process Regression in Logarithmic Time](https://mlsanity.com/paper/2102.09964/) (2021)
- [Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems](https://mlsanity.com/paper/2303.00301/) (2023)
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## Abstract (arXiv)

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.