Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
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DOI：
10.1007/s11222-018-9844-0
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发表时间：
2017-04
影响因子：
2.2
通讯作者：
E. Higson;Will Handley;M. Hobson;A. Lasenby
中科院分区：
文献类型：
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作者：
E. Higson;Will Handley;M. Hobson;A. Lasenby
我们介绍动态嵌套采样：概括的嵌套采样算法，其中的“活点”的数量不同，更有效地分配样本。在实证检验中，与相同样本数的标准嵌套抽样相比，新方法显著提高了计算精度;这种精度的提高相当于将参数估计和证据计算的计算速度提高了个因子。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，与标准嵌套抽样不同，通过持续更长时间的计算可以获得更准确的结果。流行的标准嵌套采样实现可以很容易地适应于执行动态嵌套采样，并且几个动态嵌套采样软件包现在公开可用。
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 tofor parameter estimation andfor 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.