A Monte Carlo method for uncertainty evaluation implemented on a distributed computing system

T. J. Esward, A Ginestous, P. M. Harris, I. D. Hill, S. G R Salim, I. M. Smith, B. A. Wichmann, R. Winkler, E. R. Woolliams

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


This paper is concerned with bringing together the topics of uncertainty evaluation using a Monte Carlo method, distributed computing for data parallel applications and pseudo-random number generation. A study of a measurement system to estimate the absolute thermodynamic temperatures of two high-temperature blackbodies by measuring the ratios of their spectral radiances is used to illustrate the application of these topics. The uncertainties associated with the estimates of the temperatures are evaluated and used to inform the experimental realization of the system. The difficulties associated with determining model sensitivity coefficients, and demonstrating whether a linearization of the model is adequate, are avoided by using a Monte Carlo method as an approach to uncertainty evaluation. A distributed computing system is used to undertake the Monte Carlo calculation because the computational effort required to evaluate the measurement model can be significant. In order to ensure that the results provided by a Monte Carlo method implemented on a distributed computing system are reliable, consideration is given to the approach to generating pseudo-random numbers, which constitutes a key component of the Monte Carlo procedure. © 2007 BIPM and IOP Publishing Ltd.

Original languageEnglish
Pages (from-to)319-326
Number of pages8
Issue number5
Publication statusPublished - 1 Oct 2007


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