Articlebronze2010

A two-stage procedure for determining the number of trials in the application of a Monte Carlo method for uncertainty evaluation

Gerd Wübbeler · P M Harris · M G Cox · Clemens Elster

44 citations0/0 files storedMetrologia

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Scientific Measurement and Uncertainty Evaluation Probability and Statistical Research Probabilistic and Robust Engineering Design

Abstract

Adaptive Monte Carlo schemes can be used to determine the number of Monte Carlo trials (the number of evaluations of the measurement model) necessary for the evaluation of uncertainty according to Supplement 1 to the GUM (GUM S1). The goal is to reach a prescribed numerical accuracy of the Monte Carlo results (the estimate, associated standard uncertainty and coverage interval endpoints) for a chosen confidence level. It is shown that simple sequential adaptive Monte Carlo schemes may not perform well in this regard and an alternative method based on a two-stage procedure due to Stein is proposed. The implementation of this two-stage scheme for GUM S1 is described, and its performance and robustness are demonstrated in terms of simulation results.

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2009Bayesian uncertainty analysis under prior ignorance of the measurand versus analysis using the Supplement 1 to theGuide: a comparison

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2019Monte Carlo Simulation in Uncertainty Evaluation: Strategy, Implications and Future Prospects 2021Getting started with uncertainty evaluation using the Monte Carlo method in R 2020Guidance on Bayesian uncertainty evaluation for a class of GUM measurement models 2011Monte Carlo evaluation of the uncertainty associated with the construction and use of a fitted curve 2017Comparison of Two Experiments Based on a Physical and a Torsion Pendulum to Determine the Mass Moment of Inertia Including Measurement Uncertainties 2014Validating the applicability of the GUM procedure

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