Nested Sampling Methods
Abstract
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.
Cite
@article{arxiv.2101.09675,
title = {Nested Sampling Methods},
author = {Johannes Buchner},
journal= {arXiv preprint arXiv:2101.09675},
year = {2023}
}
Comments
Published in Statistics Surveys. The open-source UltraNest package and astrostatistics tutorials can be found at https://johannesbuchner.github.io/UltraNest/