References and Acknowledgements¶
The release paper describing the code can be found here.
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. 2017b. Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation. ArXiv e-prints, 1704.03459.
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.