SuperNest: accelerated nested sampling applied to astrophysics and cosmology
Computational Physics
2022-12-06 v1 Cosmology and Nongalactic Astrophysics
Abstract
We present a method for improving the performance of nested sampling as well as its accuracy. Building on previous work by Chen et al., we show that posterior repartitioning may be used to reduce the amount of time nested sampling spends in compressing from prior to posterior if a suitable ``proposal'' distribution is supplied. We showcase this on a cosmological example with a Gaussian posterior, and release the code as an LGPL licensed, extensible Python package https://gitlab.com/a-p-petrosyan/sspr.
Cite
@article{arxiv.2212.01760,
title = {SuperNest: accelerated nested sampling applied to astrophysics and cosmology},
author = {Aleksandr Petrosyan and William James Handley},
journal= {arXiv preprint arXiv:2212.01760},
year = {2022}
}