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

## TL;DR

Dynamic nested sampling adaptively varies the number of live points during computation, significantly enhancing accuracy and efficiency for parameter estimation and evidence calculation in Bayesian analysis.

## Contribution

It introduces a flexible generalization of nested sampling that improves accuracy and efficiency, with easy adaptation and publicly available software implementations.

## Key findings

- Up to 72 times faster for parameter estimation
- Up to 7 times more accurate evidence calculations
- Enhanced accuracy for both parameter estimation and evidence

## 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.