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：模型比较与校准后的不确定性量化通常需要对参数进行积分，但对于复杂多模态目标而言，可扩展的推理具有挑战性。嵌套采样是标准MCMC方法的稳健替代方案，但其典型的顺序结构和硬约束条件使得高效的加速器实现变得困难。本文提出嵌套切片采样（NSS），一种采用碰停切片采样进行约束更新的GPU友好型向量化嵌套采样方法。通过调优分析得出一种简单的近最优规则用于设定切片宽度，从而改进高维特性并使得并行执行中每一步计算更具可预测性。在具有挑战性的合成目标、高维贝叶斯推理以及高斯过程超参数边缘化上的实验表明，NSS能够保持准确的证据估计与高质量后验样本，并且在当前最先进方法（如温度化SMC基线）可能难以处理的困难多模态问题上尤为稳健。为促进采纳与可复现性，本文发布了开源实现。