A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions

Abstract

Elliptical slice sampling, when adapted to linearly truncated multivariate normal distributions, is a rejection-free Markov chain Monte Carlo method. At its core, it requires analytically constructing an ellipse-polytope intersection. The main novelty of this paper is an algorithm that computes this intersection in $\mathcal{O}(m \log m)$ time, where $m$ is the number of linear inequality constraints representing the polytope. We show that an implementation based on this algorithm enhances numerical stability, speeds up running time, and is easy to parallelize for launching multiple Markov chains.

PDF NeurIPSW OpenReview Semantic Scholar

Cite

Text

Wu and Gardner. "A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions." NeurIPS 2024 Workshops: BDU, 2024.

Markdown

[Wu and Gardner. "A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/wu2024neuripsw-fast/)

BibTeX

@inproceedings{wu2024neuripsw-fast,
  title     = {{A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions}},
  author    = {Wu, Kaiwen and Gardner, Jacob R.},
  booktitle = {NeurIPS 2024 Workshops: BDU},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/wu2024neuripsw-fast/}
}