A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. This stabilizes MCMC proposals over time, and leads to pleasant properties, including that the number of live points and number of MCMC steps do not have to be calibrated, that the evidence and posterior approximation improves as more compute is added and can be diagnosed with convergence diagnostics from the MCMC literature. Snowballing nested sampling converges to a ``perfect'' nested sampling run with infinite number of MCMC steps.

翻译：本文提出了一种结合现实MCMC提议生成新活跃点的嵌套采样新方法。嵌套采样使用固定数量的MCMC步骤运行，随后通过雪球式嵌套采样逐步扩展至更多活跃点。该方法随时间推移稳定了MCMC提议，并展现出优良特性：无需校准活跃点数量与MCMC步数；随着计算量增加，证据值和后验近似精度持续提升；且可通过MCMC文献中的收敛诊断方法进行检测。雪球式嵌套采样最终收敛至具有无穷MCMC步数的"完美"嵌套采样过程。