论文标题
嵌套采样与高原
Nested sampling with plateaus
论文作者
论文摘要
Riley（2019）最近强调了这一点。 Schittenhelm＆Wacker（2020）认为，在可能性函数嵌套采样（NS）中存在高原的情况下会产生证据和后密度的错误估计。在非正式地解释了该问题的原因之后，我们提出了一个修改后的NS，该版本可以处理高原，并且可以追溯地应用于使用麻醉剂从流行的NS软件运行的NS运行。在修改后的NS中，高原中的实时点无需替换，而无需替换，每次驱逐后的普通NS压缩，但考虑了活点的动态数量。一旦高原中的所有点删除了现场点。我们以许多示例进行了演示。由于修改很简单，因此我们建议它成为Skilling NS算法的规范版本。
It was recently emphasised by Riley (2019); Schittenhelm & Wacker (2020) that that in the presence of plateaus in the likelihood function nested sampling (NS) produces faulty estimates of the evidence and posterior densities. After informally explaining the cause of the problem, we present a modified version of NS that handles plateaus and can be applied retrospectively to NS runs from popular NS software using anesthetic. In the modified NS, live points in a plateau are evicted one by one without replacement, with ordinary NS compression of the prior volume after each eviction but taking into account the dynamic number of live points. The live points are replenished once all points in the plateau are removed. We demonstrate it on a number of examples. Since the modification is simple, we propose that it becomes the canonical version of Skilling's NS algorithm.
