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

机译：动态嵌套采样：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：我们介绍动态嵌套采样：嵌套采样算法的概括，其中“直播点”的数量变化更有效地分配样品。在经验测试中，与具有相同数量的样品数量的标准嵌套采样相比，新方法显着提高了计算精度;这种准确性的提高相当于加速计算，以通过最高的因素与72相似，用于参数估计，并且类似于7用于证据计算。我们还表明，参数估计和证据计算的准确性可以同时提高。此外，与标准嵌套采样不同，可以通过继续计算来获得更准确的结果。流行的标准嵌套采样实现可以很容易地调整以执行动态嵌套采样，现在几个动态嵌套采样软件包现在可公开可用。

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