Nested sampling methods

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dc.contributor.authorBuchner, Johannes
dc.date.accessioned2025-01-20T20:09:24Z
dc.date.available2025-01-20T20:09:24Z
dc.date.issued2023
dc.description.abstractNested sampling (NS) computes parameter posterior distribu-tions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of complex, potentially multi -modal posteriors until a well-defined termination point. A systematic liter-ature review of nested sampling algorithms and variants is presented. We focus on complete algorithms, including solutions to likelihood-restricted prior sampling, parallelisation, termination and diagnostics. The relation between number of live points, dimensionality and computational cost is studied for two complete algorithms. A new formulation of NS is presented, which casts the parameter space exploration as a search on a tree data structure. Previously published ways of obtaining robust error estimates and dynamic variations of the number of live points are presented as special cases of this formulation. A new online diagnostic test is presented based on previous insertion rank order work. The survey of nested sampling methods concludes with outlooks for future research.
dc.fuente.origenWOS
dc.identifier.doi10.1214/23-SS144
dc.identifier.issn1935-7516
dc.identifier.urihttps://doi.org/10.1214/23-SS144
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91994
dc.identifier.wosidWOS:001022788400001
dc.language.isoen
dc.pagina.final215
dc.pagina.inicio169
dc.revistaStatistics surveys
dc.rightsacceso restringido
dc.titleNested sampling methods
dc.typeartículo
dc.volumen17
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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