Abstract
ABSTRACT 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.
Keywords
Markov chain Monte CarloSampling (signal processing)Python (programming language)Bayesian probabilityComputer scienceGibbs samplingImportance samplingMarkov chainAlgorithmMonte Carlo methodData miningStatisticsMachine learningArtificial intelligenceMathematicsProgramming language
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Publication Info
- Year
- 2020
- Type
- article
- Volume
- 493
- Issue
- 3
- Pages
- 3132-3158
- Citations
- 1926
- Access
- Closed
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Cite This
Joshua S. Speagle
(2020).
dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences.
Monthly Notices of the Royal Astronomical Society
, 493
(3)
, 3132-3158.
https://doi.org/10.1093/mnras/staa278
Identifiers
- DOI
- 10.1093/mnras/staa278
- arXiv
- 1904.02180