Nested sampling for physical scientists

Greg Ashton; Noam Bernstein; Johannes Buchner; Xi Chen; Gábor Csányi; Andrew Fowlie; Farhan Feroz; Matthew Griffiths; Will Handley; Michael Habeck; Edward Higson; Michael Hobson; Anthony Lasenby; David Parkinson; Livia B. Pártay; Matthew Pitkin; Doris Schneider; Joshua S. Speagle; Leah South; John Veitch; Philipp Wacker; David J. Wales; David Yallup

doi:10.1038/s43586-022-00121-x

Nested sampling for physical scientists

Greg Ashton
, Noam Bernstein
, Johannes Buchner
, Xi Chen
, Gábor Csányi
, Andrew Fowlie^*
, Farhan Feroz
, Matthew Griffiths
, Will Handley
, Michael Habeck
, Edward Higson
, Michael Hobson
, Anthony Lasenby
, David Parkinson
, Livia B. Pártay
, Matthew Pitkin
, Doris Schneider
, Joshua S. Speagle
, Leah South
, John Veitch

Philipp Wacker, David J. Wales, David Yallup

^*Corresponding author for this work

Monash University
King's College London
Naval Research Laboratory
Max Planck Institute for Extraterrestrial Physics
University of Bath, Department of Computer Science
University of Cambridge
Nanjing Normal University
Concr Ltd
Friedrich Schiller University Jena
The D. E. Shaw group
Korea Astronomy and Space Science Institute
University of Warwick
Lancaster University
Friedrich-Alexander University Erlangen-Nürnberg
University of Toronto
Queensland University of Technology
University of Glasgow

Research output: Contribution to journal › Article › peer-review

117 Citations (Scopus)

Abstract

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.

Original language	English
Article number	39
Journal	Nature Reviews Methods Primers
Volume	2
Issue number	1
DOIs	https://doi.org/10.1038/s43586-022-00121-x
Publication status	Published - Dec 2022
Externally published	Yes

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10.1038/s43586-022-00121-x

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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

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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",

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year = "2022",

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doi = "10.1038/s43586-022-00121-x",

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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

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

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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DO - 10.1038/s43586-022-00121-x

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AN - SCOPUS:85131638539

SN - 2662-8449

VL - 2

JO - Nature Reviews Methods Primers

JF - Nature Reviews Methods Primers

IS - 1

M1 - 39

ER -

Nested sampling for physical scientists

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