Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation

Edward Higson, Will Handley, Mike Hobson, Anthony Lasenby
4/11/2017
stat.COastro-ph.IMphysics.data-an

No official code found. Best implementation: ejhigson/dyPolyChord (22 stars, MIT, last commit 2020-05-03).

Abstract

We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of "live points" varies to allocate samples more efficiently. In empirical tests the new method significantly improves calculation accuracy compared to standard nested sampling with the same number of samples; this increase in accuracy is equivalent to speeding up the computation by factors of up to ~72 for parameter estimation and ~7 for evidence calculations. We also show that the accuracy of both parameter estimation and evidence calculations can be improved simultaneously. In addition, unlike in standard nested sampling, more accurate results can be obtained by continuing the calculation for longer. Popular standard nested sampling implementations can be easily adapted to perform dynamic nested sampling, and several dynamic nested sampling software packages are now publicly available.

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Code Implementations(1)

ejhigson/dyPolyChordCommunity100%
MIT

Super fast dynamic nested sampling with PolyChord (Python, C++ and Fortran likelihoods).

225PythonFeb 27, 20186 years agoMIT
dynamic-nested-samplingnested-sampling

Cite this paper

@article{higson2017dynamic,
  title  = {Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation},
  author = {Edward Higson and Will Handley and Mike Hobson and Anthony Lasenby},
  year   = {2017},
  eprint = {1704.03459},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1704.03459}
}

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