Jamroz, B.
and Williams, D.
(2020),
Consistency in Monte Carlo Uncertainty Analyses, Metrologia, [online], https://doi.org/10.1088/1681-7575/aba5aa
(Accessed May 21, 2025)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Consistency in Monte Carlo Uncertainty Analyses
Published
Author(s)
,
Abstract
The Monte Carlo method is an established tool that is often used to evaluate the uncertainty of measurements. For computationally challenging problems, Monte Carlo uncertainty-analyses are typically distributed across multiple processes on a multi-node cluster or supercomputer. Additionally, results from previous uncertainty analyses are often used in further analyses, leading to uncertainty analyses that are temporally distributed as well. To accurately capture the uncertainty of the output quantity of interest, Monte Carlo sample distributions must be treated consistently, using reproducible replicates, throughout the entire analysis. We highlight the need and importance of consistent and accurate Monte Carlo methods in distributed uncertainty analyses, recommend an implementation to achieve the needed consistency in distributed analyses, and discuss methods to evaluate the accuracy of implementations.Citation
Metrologia
Pub Type
Journals
Download Paper
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact [email protected].
Created July 13, 2020, Updated October 9, 2020