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.

翻译：模型比较与校准不确定性量化通常需要对参数进行积分，但对于复杂、多峰的目标函数，可扩展的推理方法往往面临挑战。嵌套采样是标准MCMC方法的稳健替代方案，但其典型的顺序化结构和硬约束条件使得在加速器上的高效实现较为困难。本文提出嵌套切片采样（NSS），这是一种面向GPU友好的向量化嵌套采样框架，采用命中-运行切片采样进行约束更新。通过调参分析，我们推导出设置切片宽度的简单近最优规则，从而改善了高维性能，并使并行执行中每步计算量更具可预测性。在具有挑战性的合成目标函数、高维贝叶斯推理以及高斯过程超参数边缘化任务上的实验表明，NSS能够保持精确的证据估计和高质量的后验样本，尤其在当前最先进方法（如回火SMC基线）难以处理的多峰问题上表现出显著鲁棒性。我们发布了开源实现以促进方法采用和结果复现。