We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows ${\tt GGNS}$ to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

翻译：我们提出了一种高性能、通用的梯度引导嵌套采样算法 ${\tt GGNS}$，该算法融合了可微分编程、哈密顿切片采样、聚类、模式分离、动态嵌套采样及并行化技术的最新进展。这种独特的组合使得 ${\tt GGNS}$ 能够很好地适应高维问题，并在多种合成与现实世界问题中展现出竞争力。我们还展示了将嵌套采样与生成流网络相结合，从而从后验分布中获取大量高质量样本的潜力。这种结合能够加速模式发现，并更准确地估计配分函数。