A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions
<|reference_start|>A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions: 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.<|reference_end|>
@article{wu2024a,
    title={A Fast, Robust Elliptical Slice Sampling Implementation for Linearly
  Truncated Multivariate Normal Distributions},
    author={Kaiwen Wu and Jacob R. Gardner},
    journal={arXiv preprint arXiv:2407.10449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10449},
    primaryClass={cs.LG stat.ML}
}