References and Acknowledgements¶
The release paper describing the code corresponding to dynesty 1.0 can be found here. We remark that more recent dynesty versions have multiple changes with respect to the paper. Therefore please ensure that you cite the paper and the specific version of dynesty you used through zenodo
A list of papers that you should cite can always be generated directly
sampler object by calling:
This will return a list of relevant papers and corresponding links to download citation information such as BibTex files.
This list will by default include the following papers:
If you use the Dynamic Nested Sampling functionality
DynamicNestedSampler), this will also include:
Dynamic Nested Sampling: Higson et al. (2019).
Depending on your specific bounding and sampling options, this may also include the following papers:
Single ellipsoid bound: Mukherjee, Parkinson & Liddle (2006).
Multiple ellipsoid bounds: Feroz, Hobson & Bridges (2009).
Random walks/staggers: Skilling (2006).
If you have utilized some of the error analysis features available through the provided utility functions (see Nested Sampling Errors), you should also cite:
Many of the plotting utilities draw heavily upon Daniel Foreman-Mackey’s wonderful corner package.
Several other plotting utilities as well as the real-time status outputs are inspired in part by features available in the statistical modeling package PyMC3.
Papers and Texts¶
The dynamic sampling framework was entirely inspired by:
Higson et al. 2019. Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation. Stat Comput, 29, 891–913, doi:10.1007/s11222-018-9844-0.
Much of the nested sampling error analysis is based on:
Buchner 2016. A statistical test for Nested Sampling algorithms. Statistics and Computing, 26, 383.
Slice sampling and its implementations in nested sampling are based on:
The implementation of multi-ellipsoidal decomposition are based in part on:
Several useful reference texts include:
Salomone et al. 2018. Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. ArXiv e-prints, 1805.03924.
Walter 2015. Point Process-based Monte Carlo estimation. ArXiv e-prints, 1412.6368.
Shaw, Bridges & Hobson 2007. Efficient Bayesian inference for multimodal problems in cosmology. MNRAS, 378, 1365.
Mukherjee, Parkinson & Liddle 2006. A Nested sampling algorithm for cosmological model selection. ApJ, 638, L51.
Silvia & Skilling 2006. Data Analysis: A Bayesian Tutorial, 2nd Edition. Oxford University Press.
Skilling 2006. Nested sampling for general Bayesian computation. Bayesian Anal., 1, 833.
Skilling 2004. Nested Sampling. In Maximum entropy and Bayesian methods in science and engineering (ed. G. Erickson, J.T. Rychert, C.R. Smith). AIP Conf. Proc., 735, 395.