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

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 increases parameter estimation and Bayesian evidence calculation accuracy by factors of up to ~8 and ~2.6 respectively compared to standard nested sampling with the same number of samples; this is equivalent to speeding up the computation by factors of ~64 and ~7. 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.