The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754
Python for scientific computing
Oliphant, T. E. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.58 · Cited by 3,303 · cited 1×
“This work has benefited from several software packages including numpy (van der Walt et al. 2011), scipy (Oliphant 2007), matplotlib (Hunter 2007), and corner (ForemanMackey 2016).”
MethodVehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2021) · Bayesian Analysis · doi:10.1214/20-ba1221 · Cited by 1,810
Cited by
Observation of Gravitational Waves from Two Neutron Star-Black Hole Coalescences
Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., et al. (2021) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/11385 · Cited by 609
Pratten, G., García-Quirós, C., Colleoni, M., Ramos-Buades, A., Estellés, H., Mateu-Lucena, M., Jaume, R., Haney, M., Keitel, D., Thompson, J. E., et al. (2021) · Physical Review D · doi:10.1103/physrevd.103.104056 · Cited by 555
Event Horizon Telescope Collaboration, Akiyama, K., Alberdi, A., Alef, W., Algaba, J. C., Anantua, R., Asada, K., Azulay, R., Bach, U., Baczko, A.-K., et al. (2022) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ac6429 · Cited by 449
Event Horizon Telescope Collaboration, Akiyama, K., Alberdi, A., Alef, W., Algaba, J. C., Anantua, R., Asada, K., Azulay, R., Bach, U., Baczko, A.-K., et al. (2022) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ac6736 · Cited by 369
“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.”
MethodCurvature tension: Evidence for a closed Universe
Handley, W. (2021) · Physical Review D · doi:10.1103/physrevd.103.l041301 · Cited by 377
Metadata
- Authors
- Speagle, J. S.
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-02-03
- Volume
- 493
- Issue
- 3
- Pages
- 3132-3158
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2931388211
- Cited by
- 2,230
- References
- 5
- FWCI
- 166.01
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. https://app.cochat.ai/papers/10.1093/mnras/staa278
- The paper
- Speagle, J. S. (2020). dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1093/mnras/staa278
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
