论文标题
使用订单统计数据嵌套采样的交叉检查
Nested sampling cross-checks using order statistics
论文作者
论文摘要
嵌套采样（NS）是现代天体物理学，宇宙学，重力波天文学和粒子物理学的数据分析中的宝贵工具。我们确定了与订单统计相关的NS的先前未使用的属性：新的实时点的插入索引应统一分发到现有的实时点。这一观察结果使我们能够创建一个新颖的单个NS运行的交叉检查。测试可以检测到NS运行何时无法从可能性函数中受约束的先验和高原的新活点进行采样，从而破坏NS的假设，从而导致不可靠的结果。我们将交叉检查应用于NS运行的玩具功能，并在2-50个维度中具有已知的分析结果，这表明我们的方法可以在各种可能性，设置和维度上检测出有问题的运行。作为现实应用程序的一个例子，我们在宇宙学模型选择的背景下进行了交叉检查的NS运行。由于交叉检查很简单，因此我们建议它成为每个适用的NS运行的强制性测试。
Nested sampling (NS) is an invaluable tool in data analysis in modern astrophysics, cosmology, gravitational wave astronomy and particle physics. We identify a previously unused property of NS related to order statistics: the insertion indexes of new live points into the existing live points should be uniformly distributed. This observation enabled us to create a novel cross-check of single NS runs. The tests can detect when an NS run failed to sample new live points from the constrained prior and plateaus in the likelihood function, which break an assumption of NS and thus leads to unreliable results. We applied our cross-check to NS runs on toy functions with known analytic results in 2 - 50 dimensions, showing that our approach can detect problematic runs on a variety of likelihoods, settings and dimensions. As an example of a realistic application, we cross-checked NS runs performed in the context of cosmological model selection. Since the cross-check is simple, we recommend that it become a mandatory test for every applicable NS run.
