提出可并行的嵌套切片采样方法，提升复杂模型的推理效率与精度。
Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference
- 用改进的切片采样实现嵌套采样的向量化，适配GPU加速
- 在高维和多峰问题上保持准确证据估计与高质量样本
- 适合需要可靠不确定性量化和复杂后验推断的研究者
模型比较和校准的不确定性量化通常需要对参数进行积分，但复杂且多峰的目标分布使得可扩展推断变得困难。嵌套采样是标准MCMC的稳健替代方法，但其通常的串行结构和硬约束使高效加速器实现困难。本文提出嵌套切片采样（NSS），一种面向GPU的向量化嵌套采样方法，采用命中-运行切片采样进行约束更新。调参分析得出了设置切片宽度的简单近优规则，提升了高维表现，并使每步计算更可预测，利于并行执行。在具有挑战性的合成目标、高维贝叶斯推断及高斯过程超参数边缘化实验中，NSS保持了准确的证据估计和高质量后验样本，在当前最先进方法（如温度化SMC基线）难以应对的多峰问题上尤其稳健。开源实现已发布，以促进采用与复现。
原文摘要 · Abstract (English)
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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