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Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation

机译：动态嵌套采样：用于参数估计和证据计算的改进算法

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摘要

We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of " live points" varies to allocate samples more efficiently. In empirical tests the new method significantly improves calculation accuracy compared to standard nested sampling with the same number of samples; this increase in accuracy is equivalent to speeding up the computation by factors of up to similar to 72 for parameter estimation and similar to 7 for evidence calculations. We also show that the accuracy of both parameter estimation and evidence calculations can be improved simultaneously. In addition, unlike in standard nested sampling, more accurate results can be obtained by continuing the calculation for longer. Popular standard nested sampling implementations can be easily adapted to perform dynamic nested sampling, and several dynamic nested sampling software packages are now publicly available.
机译：我们介绍了动态嵌套采样：嵌套采样算法的一种概括，其中“活点”的数量发生变化以更有效地分配采样。在经验测试中，与使用相同数量样本的标准嵌套样本相比，该新方法显着提高了计算精度；这种准确性的提高等同于以高达72的因数进行参数估计和近似于7的因数计算来加快计算速度。我们还表明，可以同时提高参数估计和证据计算的准确性。此外，与标准嵌套采样不同，可以通过继续进行更长的计算来获得更准确的结果。流行的标准嵌套采样实施方式可以轻松地进行动态嵌套采样，并且现已公开提供了几种动态嵌套采样软件包。

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