# Parallel Affine Transformation Tuning of Markov Chain Monte Carlo

arXiv: https://arxiv.org/abs/2401.16567 · HTML: https://mlsanity.com/paper/2401.16567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [On Sampling with Approximate Transport Maps](https://mlsanity.com/paper/2302.04763/) (2023)
- [Transport Elliptical Slice Sampling](https://mlsanity.com/paper/2210.10644/) (2022)
- [Air Markov Chain Monte Carlo](https://mlsanity.com/paper/1801.09309/) (2018)
- [Geodesic slice sampling on the sphere](https://mlsanity.com/paper/2301.08056/) (2023)
- [Reversibility of elliptical slice sampling revisited](https://mlsanity.com/paper/2301.02426/) (2023)
- [Gibbsian polar slice sampling](https://mlsanity.com/paper/2302.03945/) (2023)

## Built on by (most cited first, 2 of 2)

- [Empirical evaluation of normalizing flows in Markov Chain Monte Carlo](https://mlsanity.com/paper/2412.17136/) (2024)
- [Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling](https://mlsanity.com/paper/2609.08172/) (2026)

## Abstract (arXiv)

The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior. We explore the use of bijective affine transformations of the sample space to improve the properties of the target distribution and thereby the performance of samplers running in the transformed space. In particular, we propose a flexible and user-friendly scheme for adaptively learning the affine transformation during sampling. Moreover, the combination of our scheme with Gibbsian polar slice sampling is shown to produce samples of high quality at comparatively low computational cost in several settings based on real-world data.