dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences
2020/01/30 by Joshua S Speagle · 200 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.1093/mnras/staa278
openalex publication_date 2020/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
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.
Citations
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