Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference

• URL: http://arxiv.org/abs/2601.23252v1
• Date: Fri, 30 Jan 2026 18:20:32 GMT
• Title: Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference
• Authors: David Yallup, Namu Kroupa, Will Handley,
• Abstract summary: This paper introduces Nested Slice Sampling (NSS), a GPU-friendly, vectorized formulation of Nested Sampling.<n>A tuning analysis yields a simple near-optimal rule for setting the slice width, improving high-dimensional behavior and making per-step compute more predictable.
• Score: 0.4999814847776097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model comparison and calibrated uncertainty quantification often require integrating over parameters, but scalable inference can be challenging for complex, multimodal targets. Nested Sampling is a robust alternative to standard MCMC, yet its typically sequential structure and hard constraints make efficient accelerator implementations difficult. This paper introduces Nested Slice Sampling (NSS), a GPU-friendly, vectorized formulation of Nested Sampling that uses Hit-and-Run Slice Sampling for constrained updates. A tuning analysis yields a simple near-optimal rule for setting the slice width, improving high-dimensional behavior and making per-step compute more predictable for parallel execution. Experiments on challenging synthetic targets, high dimensional Bayesian inference, and Gaussian process hyperparameter marginalization show that NSS maintains accurate evidence estimates and high-quality posterior samples, and is particularly robust on difficult multimodal problems where current state-of-the-art methods such as tempered SMC baselines can struggle. An open-source implementation is released to facilitate adoption and reproducibility.

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