Nested sampling for physical scientists
| Title: | Nested sampling for physical scientists |
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| Authors: | 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 |
| Source: | Nature Reviews Methods Primers volume 2, Article number: 39 (2022) |
| Publication Year: | 2022 |
| Collection: | Astrophysics Condensed Matter High Energy Physics - Phenomenology Statistics |
| Subject Terms: | Statistics - Computation, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Condensed Matter - Materials Science, High Energy Physics - Phenomenology |
| More Details: | 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. Comment: 20 pages + supplementary information, 5 figures. preprint version; published version at https://www.nature.com/articles/s43586-022-00121-x |
| Document Type: | Working Paper |
| DOI: | 10.1038/s43586-022-00121-x |
| Access URL: | http://arxiv.org/abs/2205.15570 |
| Accession Number: | edsarx.2205.15570 |
| Database: | arXiv |
| DOI: | 10.1038/s43586-022-00121-x |
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