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

Authors
Handley, W 
Hobson, M 
Lasenby, A 

Loading...
Thumbnail Image
Type
Article
Change log
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.

Publication Date
2019
Online Publication Date
2018-12-03
Acceptance Date
Keywords
Nested sampling, Parameter estimation, Bayesian evidence, Bayesian computation
Journal Title
Statistics and Computing
Journal ISSN
0960-3174
1573-1375
Volume Title
29
Publisher
Springer Science and Business Media LLC
Sponsorship
STFC (1208121)