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arxiv: 2101.09675 · v4 · pith:7O7RB5G4 · submitted 2021-01-24 · stat.CO · astro-ph.IM

Nested Sampling Methods

Johannes Buchner This is my paper

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classification stat.CO astro-ph.IM
keywords samplingnestedpresentedalgorithmscompleteformulationlivemethods
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Nested sampling (NS) computes parameter posterior distributions 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 literature 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.

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