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Nested sampling for physical scientists

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arxiv 2205.15570 v1 pith:MIFNTJBS submitted 2022-05-31 stat.CO astro-ph.COastro-ph.IMcond-mat.mtrl-scihep-ph

Greg Ashton , Noam Bernstein , Johannes Buchner , Xi Chen , Gábor Csányi , Andrew Fowlie , Farhan Feroz , Matthew Griffiths
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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
This is my paper
classification stat.COastro-ph.COastro-ph.IMcond-mat.mtrl-scihep-ph
keywords samplingalgorithmnestedpracticealgorithmsapplicationappliedastronomy
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    SHARPy uses Sequential Monte Carlo with a No-U-Turn sampler in JAX to estimate gravitational-wave posteriors and evidence for binary black holes in about ten minutes.

  3. Inferring the stochastic gravitational-wave background from eccentric stellar-mass binary black holes with spaceborne detectors

    gr-qc 2025-10 conditional novelty 6.0 of 10

    Eccentric black-hole-binary backgrounds from globular clusters and isolated evolution would look like power-law noise for TianQin/LISA/Taiji, but AGN-formed binaries produce a turnover that LISA and Taiji can distinguish.

  4. Sampler-free gravitational wave inference using matrix multiplication

    gr-qc 2025-07 conditional novelty 6.0 of 10

    A new algorithm computes gravitational wave posterior distributions and evidence integrals by grid evaluation with matrix multiplications, avoiding stochastic samplers and running in minutes on a single CPU.

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