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

被引：233

作者：

Higson, Edward ^{[1
,2
]}

Handley, Will ^{[1
,2
]}

Hobson, Michael ^{[1
]}

Lasenby, Anthony ^{[1
,2
]}

机构：

[1] Univ Cambridge, Cavendish Astrophys Grp, Cambridge, England

[2] Univ Cambridge, Kavli Inst Cosmol, Cambridge, England

来源：

STATISTICS AND COMPUTING | 2019年 / 29卷 / 05期

关键词：

Nested sampling; Parameter estimation; Bayesian evidence; Bayesian computation; MONTE-CARLO; EFFICIENT;

D O I：

10.1007/s11222-018-9844-0

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

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.

引用

页码：891 / 913

页数：23

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