# State Space representation of non-stationary Gaussian Processes

arXiv: https://arxiv.org/abs/1601.01544 · HTML: https://mlsanity.com/paper/1601.01544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2016.

## Built on by (most cited first, 1 of 1)

- [Beyond State Space Representation: A General Theory for Kernel Packets](https://mlsanity.com/paper/2402.04022/) (2024)

## Abstract (arXiv)

The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest. The main reason is that it allows to compute GPs based inferences in O(n), where n is the number of observations. This implementation makes GPs suitable for Big Data. For this reason, it is important to provide a SS representation of the most important kernels used in machine learning. The aim of this paper is to show how to exploit the transient behaviour of SS models to map non-stationary kernels to SS models.