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

In: Nature Reviews Methods Primer, Vol. 2, 39 (2022), 26.05.2022.

Research output: Contribution to journalArticlepeer-review



  • Accepted Manuscript

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    Embargo ends: 26/11/22

  • 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


We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey developments in implementing efficient NS algorithms in practice in high-dimensions, including methods for sampling from the so-called constrained prior. We outline the ways in which NS may be applied and describe the application of NS in three scientific fields in which the algorithm has proved to be useful: cosmology, gravitational-wave astronomy, and materials science. We close by making recommendations for best practice when using NS and by summarizing potential limitations and optimizations of NS.
Original languageEnglish
Article number39 (2022)
JournalNature Reviews Methods Primer
Publication statusPublished - 26 May 2022
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 45655593