Nested Sampling for physical scientists

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

Research output: Contribution to journalArticlepeer-review

23 Citations (SciVal)
44 Downloads (Pure)


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 languageEnglish
Article number39
JournalNature Reviews Methods Primers
Issue number1
Publication statusPublished - 26 May 2022

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)


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