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2020

JAXNS: a high-performance nested sampling package based on JAX

Albert, Joshua G.

Understand

Since its debut by John Skilling in 2004, nested sampling has proven a valuable tool to the scientist, providing hypothesis evidence calculations and parameter inference for complicated posterior distributions, particularly in the field of astronomy.

Due to its computational complexity and long-running nature, in the past, nested sampling has been reserved for offline-type Bayesian inference, leaving tools such as variational inference and MCMC for online-type, time-constrained, Bayesian computations.
These tools do not easily handle complicated multi-modal posteriors, discrete random variables, and posteriors lacking gradients, nor do they enable practical calculations of the Bayesian evidence.
An opening thus remains for a high-performance out-of-the-box nested sampling package that can close the gap in computational time, and let nested sampling become common place in the data science toolbox.

Built on

Vegetti S., Koopmans L. V. E., Bolton A., Treu T., Gavazzi R., 2010, MNRAS , 408, 1969
1969
Earlier work this paper cites.
Chao M. T., 1982, Biometrika , 69, 653
1982
Earlier work this paper cites.
Bishop C. M., 1997, Journal of the Brazilian Computer Society , 4
1997
Earlier work this paper cites.
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Earlier work this paper cites.
Skilling J., 2004, in Fischer R., Preuss R., Toussaint U. V., eds, American Institute of Physics Conference Series Vol. 735, Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. pp 395–405, doi:10.1063/1.1835238
2004
Earlier work this paper cites.
The LIGO Scientific Collaboration et al., 2020, arXiv e-prints, p. arXiv:2004.08342
Original
2004
Earlier work this paper cites.
Skilling J., 2005, in Knuth K. H., Abbas A. E., Morris R. D., Castle J. P., eds, American Institute of Physics Conference Series Vol. 803, Bayesian Inference and Maximum Entropy Methods in Science and Engineering. pp 3–24, doi:10.1063/1.2149776
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Earlier work this paper cites.
Mukherjee P., Parkinson D., Liddle A. R., 2006, ApJ , 638, L51
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Earlier work this paper cites.
Skilling J., 2006, Bayesian Anal. , 1, 833
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Earlier work this paper cites.
Eriksen H. K., Banday A. J., Górski K. M., Hansen F. K., Lilje P. B., 2007, ApJ , 660, L81
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Earlier work this paper cites.
Feroz F., Hobson M. P., 2008, MNRAS , 384, 449
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Earlier work this paper cites.
Brewer B. J., Pártay L. B., Csányi G., 2009, arXiv e-prints, p. arXiv:0912.2380
Original
2009
Earlier work this paper cites.
Feroz F., Hobson M. P., Bridges M., 2009, MNRAS , 398, 1601
2009
Earlier work this paper cites.
Hoftuft J., Eriksen H. K., Banday A. J., Górski K. M., Hansen F. K., Lilje P. B., 2009, ApJ , 699, 985
2009
Earlier work this paper cites.

Similar

Aird J., et al., 2010, MNRAS , 401, 2531
2010
Cited alongside, same era.
Simpson F., Lalchand V., Rasmussen C. E., 2020, arXiv e-prints, p. arXiv:2010.16344
Original
2010
Cited alongside, same era.
Barnabè M., Czoske O., Koopmans L. V. E., Treu T., Bolton A. S., 2011, MNRAS , 415, 2215
2011
Cited alongside, same era.
Betancourt M., 2011, in Mohammad-Djafari A., Bercher J.-F., Bessiére P., eds, American Institute of Physics Conference Series Vol. 1305, Bayesian Inference and Maximum Entropy Methods in Science and Engineering. pp 165–172 ( arXiv:1005.0157 ), doi:10.1063/1.3573613
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2011
Cited alongside, same era.
Hoffman M. D., Gelman A., 2011, arXiv e-prints, p. arXiv:1111.4246
Original
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Cited alongside, same era.

Then

Corsaro E., De Ridder J., 2015, in European Physical Journal Web of Conferences. p. 06019 ( arXiv:1509.08311 ), doi:10.1051/epjconf/201510106019
Original
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Later among the works it cites.
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Seide F., Agarwal A., 2016, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. Association for Computing Machinery, New York, NY, USA, p. 2135, doi:10.1145/2939672.2945397 , https://doi.org/10.1145/2939672.2945397
2016
Later among the works it cites.
The Theano Development Team et al., 2016, arXiv e-prints, p. arXiv:1605.02688

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