@article{1743747618b84d9b929f09eb2a9261e2,
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 Farhan Feroz and Andrew Fowlie and Matthew Griffiths and Michael Habeck and Will Handley and Edward Higson and Michael Hobson and Anthony Lasenby and Parkinson, {David B.} and P{\'a}rtay, {Livia B.} and Matthew Pitkin and Doris Schneider and Leah South and Joshua Speagle and John Veitch and Philipp Wacker and David Wales and David Yallup",
note = "Funding Information: The authors thank J. Skilling for his wonderful algorithm. The success of nested sampling may be that simple beats clever; but the beauty of nested sampling is that it is both simple and clever. They thank K. Barbary for discussions. A.F. was supported by a National Natural Science Foundation of China (NSFC) Research Fund for International Young Scientists (grant 11950410509). L.B.P. acknowledges support from the Engineering and Physical Sciences Research Council (EPSRC) through an Early Career Fellowship (EP/T000163/1). M. Habeck acknowledges support from the Carl Zeiss Foundation. N.B. was funded by the US Naval Research Laboratory{\textquoteright}s base 6.1 research program, and CPU time from the US Department of Defence (DoD) High Performance Computing Modernization Program Office (HPCMPO) at the Air Force Research Laboratory (AFRL) and Army Research Laboratory (ARL) DoD Supercomputing Research Centers (DSRCs). M.P. acknowledges support from the Science and Technology Facilities Council (STFC) (ST/V001213/1 and ST/V005707/1). W.H. was supported by a Royal Society University Research Fellowship. Publisher Copyright: {\textcopyright} 2022, Springer Nature Limited.",
year = "2022",
month = may,
day = "26",
doi = "10.1038/s43586-022-00121-x",
language = "English",
volume = "2",
journal = "Nature Reviews Methods Primers",
issn = "2662-8449",
publisher = "Springer Nature",
number = "1",
}