A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions
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Abstract (arXiv)
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 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 15 Jul 2024.
Builds on
4 of 13 references.
References · 4
- 2023Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processescited by 19
- 2021Evaluating State-of-the-Art Classification Models Against Bayes Optimalitycited by 16
- 2019Integrals over Gaussians under Linear Domain Constraintscited by 15
- 2020A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processescited by 6
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Citing papers per year: 2026: 1.
1 paper.
- 2026Knowledge Gradient for Preference Learningcited by 1
- 2026Knowledge Gradient for Preference Learning29 Jan 2026