# Parallel Iterated Extended and Sigma-point Kalman Smoothers

arXiv: https://arxiv.org/abs/2102.00514 · HTML: https://mlsanity.com/paper/2102.00514/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2021.

## Built on by (most cited first, 6 of 6)

- [Temporal Parallelization of Inference in Hidden Markov Models](https://mlsanity.com/paper/2102.05743/) (2021)
- [Parallel-in-Time Probabilistic Numerical ODE Solvers](https://mlsanity.com/paper/2310.01145/) (2023)
- [De-Sequentialized Monte Carlo: a parallel-in-time particle smoother](https://mlsanity.com/paper/2202.02264/) (2022)
- [Temporal Gaussian Process Regression in Logarithmic Time](https://mlsanity.com/paper/2102.09964/) (2021)
- [Linear-Time Probabilistic Solutions of Boundary Value Problems](https://mlsanity.com/paper/2106.07761/) (2021)
- [Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems](https://mlsanity.com/paper/2303.00301/) (2023)

## Abstract (arXiv)

The problem of Bayesian filtering and smoothing in nonlinear models with additive noise is an active area of research. Classical Taylor series as well as more recent sigma-point based methods are two well-known strategies to deal with these problems. However, these methods are inherently sequential and do not in their standard formulation allow for parallelization in the time domain. In this paper, we present a set of parallel formulas that replace the existing sequential ones in order to achieve lower time (span) complexity. Our experimental results done with a graphics processing unit (GPU) illustrate the efficiency of the proposed methods over their sequential counterparts.