论文标题
最高：应用于天体物理学和宇宙学的加速嵌套采样
SuperNest: accelerated nested sampling applied to astrophysics and cosmology
论文作者
论文摘要
我们提出了一种改善嵌套性能的方法 采样及其准确性。以先前的工作为基础 Chen等人，我们证明了后验重新分配 可用于减少嵌套抽样花费的时间 如果合适的``提议''从后部进行压缩 提供分配。我们在宇宙学的例子上展示了这一点 使用高斯后部，并以LGPL许可发布代码， 可扩展的Python软件包 https://gitlab.com/a-p-petrosyan/sspr。
We present a method for improving the performance of nested sampling as well as its accuracy. Building on previous work by Chen et al., we show that posterior repartitioning may be used to reduce the amount of time nested sampling spends in compressing from prior to posterior if a suitable ``proposal'' distribution is supplied. We showcase this on a cosmological example with a Gaussian posterior, and release the code as an LGPL licensed, extensible Python package https://gitlab.com/a-p-petrosyan/sspr.
