Measurement Uncertainty And Probability PdfBy Avelaine B. In and pdf 03.05.2021 at 09:03 4 min read
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- Assessing the measurement uncertainty of qualitative analysis in the clinical laboratory
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- Probability and Bayesian Modeling
We investigate the influence of finite resolution on measurement uncertainty from the perspective of the Guide to the Expression of Uncertainty in Measurement GUM. Finite resolution in a measurement that is perturbed by Gaussian noise yields a distribution of results that strongly depends on the location of the true value relative to the resolution increment. We show that there is no simple expression relating the standard deviation of the distribution of measurement results to the associated uncertainty at a specified level of confidence.
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Assessing the measurement uncertainty of qualitative analysis in the clinical laboratory
Forbes npl. Received: 5 May Accepted: 27 June Both documents describe three phases a the construction of a measurement model, b the assignment of probability distributions to quantities, and c a computational phase that specifies the distribution for the quantity of interest, the measurand. The two approaches described in these two documents agree in the first two phases but employ different computational approaches, with the GUM using linearisations to simplify the calculations. Recent years have seen an increasing interest in using Bayesian approaches to evaluating measurement uncertainty. The Bayesian approach in general differs in the assignment of the probability distributions and its computational phase usually requires Markov chain Monte Carlo MCMC approaches. In this paper, we summarise the three approaches to evaluating measurement uncertainty and show how we can regard the GUM and GUMS1 as providing approximate solutions to the Bayesian approach.
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Journal ID publisher-id : BM. ISSN print : ISSN electronic : Copyright: , Croatian Society of Medical Biochemistry. License open-access :. Publication date print and electronic : 15 February Electronic Location Identifier:
Monte Carlo simulation for the evaluation of measurement uncertainty of pharmaceutical certified reference materials. Werickson F. The supplemental Guide to the Expression of Uncertainty Measurement , which deals with the propagation of distributions, encourages the use of the Monte Carlo simulation MCS for estimating the uncertainty of measurands. This paper describes the application of this method to estimate the measurement uncertainty of active pharmaceutical ingredient API mass fractions of two certified reference materials CRMs : metronidazole and captopril. Keywords: Monte Carlo simulation, evaluation of measurement uncertainty, certified reference materials, active pharmaceutical ingredients.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A Review of Relationships Between Possibility and Probability Representations of Uncertainty in Measurement Abstract: The main advances regarding the deep connections between probability and possibility measurement uncertainty representation but not the propagation over the last decade are reviewed. They concern the following: the definition of a possibility distribution equivalent to a probability of one from its whole set of dispersion intervals about one point for all of the probability levels, the bridges with the conventional dispersion parameters, the representation of a partial probability knowledge owing to a maximum specificity principle better than the maximum entropy principle, and also probability inequalities. The use of a possibility representation for common measurement situations such as the description of measurement results, measurand estimation, and expression of a priori uncertainty information is illustrated and then discussed in view of their use in further processing propagation and fuzzy inference systems.
Measurement uncertainties can come from the measuring instrument, from The spread of a set of values can take different forms, or probability distributions.
Probability and Bayesian Modeling
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