# Sampling Errors in Nested Sampling Parameter Estimation

## TL;DR

This paper investigates the sources of sampling errors in nested sampling parameter estimation, introduces a new diagrammatic representation, and proposes a novel algorithm to accurately measure these errors, validated through empirical tests.

## Contribution

It identifies the main sources of sampling errors in nested sampling parameter estimation and introduces a new algorithm for accurately measuring these errors in single runs.

## Key findings

- Identified two main sources of sampling errors in nested sampling.
- Current methods cannot accurately measure errors in a single run.
- Proposed a new algorithm validated through empirical verification.

## Abstract

Sampling errors in nested sampling parameter estimation differ from those in Bayesian evidence calculation, but have been little studied in the literature. This paper provides the first explanation of the two main sources of sampling errors in nested sampling parameter estimation, and presents a new diagrammatic representation for the process. We find no current method can accurately measure the parameter estimation errors of a single nested sampling run, and propose a method for doing so using a new algorithm for dividing nested sampling runs. We empirically verify our conclusions and the accuracy of our new method.