References and Acknowledgements

The release paper describing the code can be found here.

A list of papers that you should cite can always be generated directly from the sampler object by calling:

print(sampler.citations)

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 (via DynamicNestedSampler), this will also include:

Depending on your specific bounding and sampling options, this may also include the following papers:

If you have utilized some of the error analysis features available through the provided utility functions (see Nested Sampling Errors), you should also cite:

Code

dynesty is the spiritual successor to Nested Sampling package nestle and has benefited enormously from the work put in by Kyle Barbary and other contributors.

Much of the API is inspired by the ensemble MCMC package emcee as well as other work by Daniel Foreman-Mackey.

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:

Higson et al. 2018. Sampling errors in nested sampling parameter estimation. Bayesian Analysis, 13, no. 3, 873–896, doi:10.1214/17-BA1075.

Chopin & Robert 2010. Properties of Nested Sampling. Biometrika, 97, 741.

The nested sampling algorithms in RadFriendsSampler and SupFriendsSampler are 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:

Handley, Hobson & Lasenby 2015b. POLYCHORD: next-generation nested sampling. MNRAS, 453, 4384.

Handley, Hobson & Lasenby 2015a. POLYCHORD: nested sampling for cosmology. MNRASL, 450, L61.

Neal 2003. Slice sampling. Ann. Statist., 31, 705.

The implementation of multi-ellipsoidal decomposition are based in part on:

Feroz et al. 2013. Importance Nested Sampling and the MultiNest Algorithm. ArXiv e-prints, 1306.2144.

Feroz, Hobson & Bridges 2009. MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. MNRAS, 398, 1601.

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.