# Robust Inference for State-Space Models with Skewed Measurement Noise

arXiv: https://arxiv.org/abs/1503.06606 · HTML: https://mlsanity.com/paper/1503.06606/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2015.

## Built on by (most cited first, 3 of 3)

- [Outlier-robust Kalman Filtering through Generalised Bayes](https://mlsanity.com/paper/2405.05646/) (2024)
- [Von Mises-Fisher Elliptical Distribution](https://mlsanity.com/paper/2103.07948/) (2021)
- [Robust Filtering and Learning in State-Space Models: Skewness and Heavy Tails Via Asymmetric Laplace Distribution](https://mlsanity.com/paper/2507.22343/) (2025)

## Abstract (arXiv)

Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.