论文标题
嵌套抽样统计错误
Nested sampling statistical errors
论文作者
论文摘要
嵌套采样（NS）是用于贝叶斯计算的流行算法。我们通过分析和数值研究NS的统计误差。我们展示了两个分析结果。首先，我们表明，使用信息理论在Skilling表达式中的主要术语与Keeton表达中的主要术语相匹配，从时光分析中。此近似协议以前仅在数字上是已知的，并且有些神秘。其次，我们表明单个NS的不确定性大致等于重复NS运行中的标准偏差。尽管直观，但以前是理所当然的。我们通过在几个数值示例中调查结果及其假设结束，包括NS不确定性增加而无约束力的情况。
Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling's expression using information theory match the leading terms in Keeton's expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. Whilst intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound.
