Ashton, G., Bernstein, N., Buchner, J., Chen, X., Csányi, G., Fowlie, A., Feroz, F., Griffiths, M., Handley, W., Habeck, M., Higson, E., Hobson, M., Lasenby, A., Parkinson, D., Pártay, L. B., Pitkin, M., Schneider, D., Speagle, J. S., South, L., ... Yallup, D. (2022). Nested sampling for physical scientists. Nature Reviews Methods Primers, 2(1), Article 39. https://doi.org/10.1038/s43586-022-00121-x
Ashton, Greg ; Bernstein, Noam ; Buchner, Johannes et al. / Nested sampling for physical scientists. In: Nature Reviews Methods Primers. 2022 ; Vol. 2, No. 1.
@article{cd6f7c72df5f4c7eabd7cab9ccde0d85,
title = "Nested sampling for physical scientists",
abstract = "This Primer examines Skilling{\textquoteright}s nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarized and recent developments using efficient nested sampling algorithms in high dimensions surveyed, including methods for sampling from the constrained prior. Different ways of applying nested sampling are outlined, with detailed examples from three scientific fields: cosmology, gravitational-wave astronomy and materials science. Finally, the Primer includes recommendations for best practices and a discussion of potential limitations and optimizations of nested sampling.",
author = "Greg Ashton and Noam Bernstein and Johannes Buchner and Xi Chen and G{\'a}bor Cs{\'a}nyi and Andrew Fowlie and Farhan Feroz and Matthew Griffiths and Will Handley and Michael Habeck and Edward Higson and Michael Hobson and Anthony Lasenby and David Parkinson and P{\'a}rtay, {Livia B.} and Matthew Pitkin and Doris Schneider and Speagle, {Joshua S.} and Leah South and John Veitch and Philipp Wacker and Wales, {David J.} and David Yallup",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Limited.",
year = "2022",
month = dec,
doi = "10.1038/s43586-022-00121-x",
language = "English",
volume = "2",
journal = "Nature Reviews Methods Primers",
issn = "2662-8449",
number = "1",
}
Ashton, G, Bernstein, N, Buchner, J, Chen, X, Csányi, G, Fowlie, A, Feroz, F, Griffiths, M, Handley, W, Habeck, M, Higson, E, Hobson, M, Lasenby, A, Parkinson, D, Pártay, LB, Pitkin, M, Schneider, D, Speagle, JS, South, L, Veitch, J, Wacker, P, Wales, DJ & Yallup, D 2022, 'Nested sampling for physical scientists', Nature Reviews Methods Primers, vol. 2, no. 1, 39. https://doi.org/10.1038/s43586-022-00121-x
Nested sampling for physical scientists. / Ashton, Greg; Bernstein, Noam; Buchner, Johannes et al.
In:
Nature Reviews Methods Primers, Vol. 2, No. 1, 39, 12.2022.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Nested sampling for physical scientists
AU - Ashton, Greg
AU - Bernstein, Noam
AU - Buchner, Johannes
AU - Chen, Xi
AU - Csányi, Gábor
AU - Fowlie, Andrew
AU - Feroz, Farhan
AU - Griffiths, Matthew
AU - Handley, Will
AU - Habeck, Michael
AU - Higson, Edward
AU - Hobson, Michael
AU - Lasenby, Anthony
AU - Parkinson, David
AU - Pártay, Livia B.
AU - Pitkin, Matthew
AU - Schneider, Doris
AU - Speagle, Joshua S.
AU - South, Leah
AU - Veitch, John
AU - Wacker, Philipp
AU - Wales, David J.
AU - Yallup, David
N1 - Publisher Copyright:
© 2022, Springer Nature Limited.
PY - 2022/12
Y1 - 2022/12
N2 - This Primer examines Skilling’s nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarized and recent developments using efficient nested sampling algorithms in high dimensions surveyed, including methods for sampling from the constrained prior. Different ways of applying nested sampling are outlined, with detailed examples from three scientific fields: cosmology, gravitational-wave astronomy and materials science. Finally, the Primer includes recommendations for best practices and a discussion of potential limitations and optimizations of nested sampling.
AB - This Primer examines Skilling’s nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarized and recent developments using efficient nested sampling algorithms in high dimensions surveyed, including methods for sampling from the constrained prior. Different ways of applying nested sampling are outlined, with detailed examples from three scientific fields: cosmology, gravitational-wave astronomy and materials science. Finally, the Primer includes recommendations for best practices and a discussion of potential limitations and optimizations of nested sampling.
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U2 - 10.1038/s43586-022-00121-x
DO - 10.1038/s43586-022-00121-x
M3 - Article
AN - SCOPUS:85131638539
SN - 2662-8449
VL - 2
JO - Nature Reviews Methods Primers
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ER -
Ashton G, Bernstein N, Buchner J, Chen X, Csányi G, Fowlie A et al. Nested sampling for physical scientists. Nature Reviews Methods Primers. 2022 Dec;2(1):39. doi: 10.1038/s43586-022-00121-x
