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 VeitchPhilipp Wacker, David J. Wales, David Yallup

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 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 languageEnglish
Article number39
JournalNature Reviews Methods Primers
Volume2
Issue number1
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

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