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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › 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 - The Author's Accepted Manuscript (the accepted version of the manuscript as submitted by the author) may only be posted 6 months after the paper is published, consistent with our self-archiving embargo. Please note that the Author’s Accepted Manuscript may not be released under a Creative Commons license. For Nature Research Terms of Reuse of archived manuscripts please see: http://www.nature.com/authors/policies/license.html#terms
PY - 2022/12/31
Y1 - 2022/12/31
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.
U2 - 10.1038/s43586-022-00121-x
DO - 10.1038/s43586-022-00121-x
M3 - Journal article
VL - 2
JO - Nature Reviews Methods Primers
JF - Nature Reviews Methods Primers
SN - 2662-8449
IS - 1
M1 - 39
ER -