Nested sampling is widely used in astrophysics for reliably inferring model parameters and comparing models within a Bayesian framework. To address models with many parameters, Markov Chain Monte Carlo (MCMC) random walks are incorporated within nested sampling to advance a live point population. Diagnostic tools for nested sampling are crucial to ensure the reliability of astrophysical conclusions. We develop a diagnostic to identify problematic random walks that fail to meet the requirements of nested sampling. The distance from the start to the end of the random walk, the jump distance, is divided by the typical neighbor distance between live points, computed robustly with the MLFriends algorithm, to obtain a relative jump distance (RJD). We propose the geometric mean RJD and the fraction of RJD>1 as new summary diagnostics. Relative jump distances are investigated with mock and real-world inference applications, including inferring the distance to gravitational wave event GW170817. Problematic nested sampling runs are identified based on significant differences to reruns with much longer MCMC chains. These consistently exhibit low average RJDs and f(RJD>1) values below 50 percent. The RJD is more sensitive than previous tests based on the live point insertion order. The RJD diagnostic is proposed as a widely applicable diagnostic to verify inference with nested sampling. It is implemented in the UltraNest package in version 4.1.

翻译：摘要：嵌套抽样广泛应用于天体物理学中，用于在贝叶斯框架下可靠推断模型参数并比较不同模型。为处理含多个参数的模型，嵌套抽样中引入了马尔可夫链蒙特卡洛（MCMC）随机游走，以推动活跃点种群的进化。嵌套抽样的诊断工具对于确保天体物理学结论的可靠性至关重要。我们开发了一种诊断方法，用于识别未能满足嵌套抽样要求的异常随机游走。将从随机游走起点到终点的距离（即跳跃距离）除以使用MLFriends算法稳健计算的活跃点之间典型邻近距离，得到相对跳跃距离（RJD）。我们提出几何平均RJD和RJD>1的比例作为新的汇总诊断指标。通过模拟和实际推理应用（包括推断引力波事件GW170817的距离）对相对跳跃距离进行了研究。通过与使用更长MCMC链的重复运行结果存在显著差异，可识别出异常的嵌套抽样运行。这些运行始终表现出较低的平均RJD和低于50%的f(RJD>1)值。RJD比基于活跃点插入顺序的先前测试更为敏感。RJD诊断被提议作为一种广泛适用的验证嵌套抽样推理的方法，并在UltraNest包4.1版本中实现。