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

By Joshua S. Speagle. Monthly notices of the Royal Astronomical Society, 2019

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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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4c9c19f5d1425a5b43382a93beef638263958b84

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Citations in the corpus

GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run (2020, methodology): “Parameter estimation was performed with the LALInference [179] and LALSimulation libraries within LALSuite [295], the Bilby and Parallel Bilby libraries [232–234] using the dynesty nested sampling package [235], and the RIFT library [229–231].”
Observation of Gravitational Waves from the Coalescence of a 2.5–4.5 M ⊙ Compact Object and a Neutron Star (2024, methodology): “To generate samples from the hierarchical likelihood for our population analyses we use the Dynesty nested sampler (Speagle 2020) as implemented in GWPopulation (Talbot et al. 2019) for the Power law + Dip + Break and NSBH-pop analyses and the Hamiltonian Monte Carlo sampler implemented in the PyMC…”
First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass (2022, methodology): “Unless otherwise specified, we use eht-imaging to fit to visibility amplitudes and closure phases, so that the snapshot likelihood is given by Equation (32), and we use dynesty (Speagle 2020) for posterior exploration and evidence estimation.”
Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue (2020, methodology): “We use dynesty (Speagle 2020) as our sampler, with 2000 live points and 100 walks.”
High Tide or Riptide on the Cosmic Shoreline? A Water-rich Atmosphere or Stellar Contamination for the Warm Super-Earth GJ 486b from JWST Observations (2023, methodology): “We fitted both models to each data set using the dynesty nested sampling code for Bayesian inference (Speagle 2020) and then used the Bayesian evidence to calculate the Bayes factor of each model (e.g., Trotta 2008, 2017).”
Stellar Population Inference with Prospector (2020, methodology): “Nested sampling in Prospector uses the dynesty pure-Python code package (Speagle 2020), an implementation of algorithms described in Feroz et al. (2009); Higson et al. (2019).”
Evidence for Hierarchical Black Hole Mergers in the Second LIGO–Virgo Gravitational Wave Catalog (2020, methodology): “We generate posterior samples for population hyperparameters Λ using the nested sampler DYNESTY (Speagle 2020) using the GWPOPULATION framework (Talbot et al. 2019), which takes 13 Posterior samples for GW190412, GW190521, and the other GWTC-2 systems are available from…”
The JWST Early Release Observations (2022, methodology): “We used the dynamic nested sampling algorithm implemented in the dynesty package as described in Speagle (2020) to sample from the posterior distribution of the parameters.”