DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidences

被引：1514

作者：

Speagle, Joshua S. ^{[1
]}

机构：

[1] Harvard Smithsonian Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA

来源：

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | 2020年 / 493卷 / 03期

关键词：

methods: data analysis; methods: statistical; MONTE-CARLO METHODS; INFERENCE; EFFICIENT; TOOL; SKY;

D O I：

10.1093/mnras/staa278

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We present DYNESTY, a public, open-source, PYTHON package to estimate Bayesian posteriors and evidences (marginal likelihoods) using the dynamic nested sampling methods developed by Higson et al. By adaptively allocating samples based on posterior structure, dynamic nested sampling has the benefits of Markov chain Monte Carlo (MCMC) algorithms that focus exclusively on posterior estimation while retaining nested sampling's ability to estimate evidences and sample from complex, multimodal distributions. We provide an overview of nested sampling, its extension to dynamic nested sampling, the algorithmic challenges involved, and the various approaches taken to solve them in this and previous work. We then examine DYNESTY's performance on a variety of toy problems along with several astronomical applications. We find in particular problems DYNESTY can provide substantial improvements in sampling efficiency compared to popular MCMC approaches in the astronomical literature. More detailed statistical results related to nested sampling are also included in the appendix.

引用

页码：3132 / 3158

页数：27

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