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% Creation date: 2022-11-29
% Creation time: 08-44-08
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% 64
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@Article { KokWE2022,
title = {Impact of Imperfect Artefacts and the Modus Operandi on
Uncertainty Quantification Using Virtual Instruments},
journal = {Metrology},
year = {2022},
month = {6},
day = {12},
volume = {2},
pages = {311--319},
tags = {8.4,8.42,Messunsicherheit,Form},
DOI = {10.3390/metrology2020019},
author = {Kok, G and W{\"u}bbeler, G and Elster, C}
}
@Article { MarschallSSE2022,
title = {Uncertainty propagation in quantitative magnetic force microscopy using a Monte-Carlo method},
journal = {IEEE Transactions on Magnetics},
year = {2022},
month = {2},
day = {21},
pages = {1--1},
tags = {8.4,8.42,Messunsicherheit},
DOI = {10.1109/TMAG.2022.3153176},
author = {Marschall, M and Sievers, S and Schumacher, H W and Elster, C}
}
@Article { MarschallWE2022,
title = {Rejection sampling for Bayesian uncertainty evaluation using the Monte Carlo techniques of GUM-S1},
journal = {Metrologia},
year = {2022},
month = {2},
day = {1},
volume = {59},
number = {1},
pages = {015004},
tags = {8.4,8.42,Messunsicherheit},
DOI = {10.1088/1681-7575/ac3920},
author = {Marschall, M and W{\"u}bbeler, G and Elster, C}
}
@Article { GruberDSEE2022,
title = {Discrete wavelet transform on uncertain data: Efficient online implementation for practical applications.},
journal = {Advanced Mathematical and Computational Tools in Metrology and Testing XII, Series on Advances in Mathematics for Applied Sciences},
year = {2022},
month = {1},
day = {30},
volume = {90},
tags = {8.4,8.42,Messunsicherheit},
ISSN = {978-981-1242-37-3},
author = {Gruber, M and Dorst, T and Sch{\"u}tze, A and Eichst{\"a}dt, S and Elster, C}
}
@Phdthesis { Metzner2021,
title = {Bayesian data analysis for magnetic resonance fingerprinting},
year = {2021},
month = {12},
day = {7},
keywords = {publiziert},
tags = {8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna},
school = {TU Berlin},
type = {PhD Thesis},
DOI = {10.14279/depositonce-12455},
author = {Metzner, S}
}
@Article { MartinE2021,
title = {GUI for Bayesian sample size planning in type A uncertainty evaluation},
journal = {Measurement Science and Technology},
year = {2021},
month = {4},
day = {30},
volume = {32},
number = {7},
pages = {5005},
tags = {8.4,8.42,Messunsicherheit},
DOI = {10.1088/1361-6501/abe2bd},
author = {Martin, J and Elster, C}
}
@Article { MetznerWFGKE2021,
title = {Bayesian uncertainty quantification for magnetic resonance fingerprinting},
journal = {Physics in Medicine \& Biology},
year = {2021},
month = {3},
day = {1},
volume = {66},
number = {7},
pages = {075006},
tags = {8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna},
DOI = {10.1088/1361-6560/abeae7},
author = {Metzner, S and W{\"u}bbeler, G and Flassbeck, S and Gatefait, C and Kolbitsch, C and Elster, C}
}
@Article { WubbelerME2020,
title = {A simple method for Bayesian uncertainty evaluation in linear models},
journal = {Metrologia},
year = {2020},
month = {10},
day = {21},
volume = {57},
number = {6},
pages = {065010},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1088/1681-7575/aba3b8},
author = {W{\"u}bbeler, G and Marschall, M and Elster, C}
}
@Article { MartinE2020_2,
title = {The variation of the posterior variance and Bayesian sample size determination},
journal = {Statistical Methods \& Applications},
year = {2020},
month = {8},
day = {25},
pages = {1613-981X},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1007/s10260-020-00545-3},
author = {Martin, J and Elster, C}
}
@Article { DemeyerFE2020,
title = {Guidance on Bayesian uncertainty evaluation for a class of GUM measurement models},
journal = {Metrologia},
year = {2020},
month = {8},
day = {18},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1088/1681-7575/abb065},
author = {Demeyer, S and Fischer, N and Elster, C}
}
@Article { BartlEMSVW2020,
title = {Thermal expansion and compressibility of single-crystal silicon between 285 K and 320 K},
journal = {Measurement Science and Technology},
year = {2020},
month = {4},
day = {3},
volume = {31},
number = {6},
tags = {8.4,8.42,Unsicherheit,Regression},
DOI = {10.1088/1361-6501/ab7359},
author = {Bartl, G and Elster, C and Martin, J and Sch{\"o}del, R and Voigt, M and Walkov, A}
}
@Article { WubbelerE2020,
title = {On the transferability of the GUM-S1 type A uncertainty},
journal = {Metrologia},
year = {2020},
month = {1},
day = {23},
volume = {57},
number = {1},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1088/1681-7575/ab50d6},
author = {W{\"u}bbeler, G and Elster, C}
}
@Article { KlauenbergWE2019,
title = {About not Correcting for Systematic Effects},
journal = {Measurement Science Review},
year = {2019},
month = {9},
day = {30},
volume = {19},
number = {5},
pages = {204--208},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.2478/msr-2019-0026},
author = {Klauenberg, K and W{\"u}bbeler, G and Elster, C}
}
@Article { MetznerWE2018,
title = {Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging},
journal = {AStA Adv Stat Anal},
year = {2019},
month = {8},
day = {29},
volume = {103},
number = {3},
pages = {333--355},
tags = {8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna},
DOI = {10.1007/s10182-018-00334-0},
author = {Metzner, S and W{\"u}bbeler, G and Elster, C}
}
@Article { MartinBE2019,
title = {Application of Bayesian model averaging to the determination of thermal expansion of single-crystal silicon},
journal = {Measurement Science and Technology},
year = {2019},
month = {2},
day = {21},
volume = {30},
pages = {045012},
tags = {8.4,8.42,Unsicherheit,Regression},
DOI = {10.1088/1361-6501/ab094b},
author = {Martin, J and Bartl, G and Elster, C}
}
@Article { BodnarE2016,
title = {Assessment of vague and noninformative priors for Bayesian estimation of the realized random effects in random-effects meta-analysis},
journal = {AStA Advances in Statistical Analysis},
year = {2018},
month = {1},
day = {31},
volume = {102},
number = {1},
pages = {1--20},
tags = {8.42,KC,Unsicherheit},
DOI = {10.1007/s10182-016-0279-7},
author = {Bodnar, O and Elster, C}
}
@Article { WubbelerBE2018,
title = {Robust Bayesian linear regression with application to an analysis of the CODATA values for the Planck constant},
journal = {Metrologia},
year = {2018},
month = {1},
day = {2},
volume = {55},
number = {1},
pages = {20},
tags = {8.4,8.42,Unsicherheit,Regression},
DOI = {10.1088/1681-7575/aa98aa},
author = {W{\"u}bbeler, G and Bodnar, O and Elster, C}
}
@Article { SchmahlingWKRSTSE2017,
title = {Uncertainty evaluation and propagation for spectral measurements},
journal = {Color, Research and Application},
year = {2017},
month = {9},
day = {26},
volume = {43},
number = {1},
pages = {6--16},
tags = {8.4,8.42,Unsicherheit,LargeScaleDataAna},
DOI = {10.1002/col.22185},
author = {Schm{\"a}hling, F and W{\"u}bbeler, G and Kr{\"u}ger, U and Ruggaber, B and Schmidt, F and Taubert, R D and Sperling, A and Elster, C}
}
@Article { ReginattoAE2017,
title = {Assessment of CT image quality using a Bayesian approach},
journal = {Metrologia},
year = {2017},
month = {6},
day = {14},
volume = {54},
number = {4},
pages = {S74--S82},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1088/1681-7575/aa735b},
author = {Reginatto, M and Anton, M and Elster, C}
}
@Article { EichstadtW2017,
title = {Evaluation of uncertainty for regularized deconvolution: A case study in hydrophone measurements},
journal = {J. Acoust. Soc. Am.},
year = {2017},
month = {6},
day = {6},
volume = {141},
number = {6},
pages = {4155--4167},
tags = {8.4,8.42,Unsicherheit,Dynamik},
DOI = {10.1121/1.4983827},
author = {Eichst{\"a}dt, S and Wilkens, V}
}
@Article { BodnarBE2017,
title = {Bayesian inference for measurements of ionizing radiation under partial information},
journal = {Metrologia},
year = {2017},
month = {5},
day = {11},
volume = {54},
number = {3},
pages = {S29--S33},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1088/1681-7575/aa69ad},
author = {Bodnar, O and Behrens, R and Elster, C}
}
@Article { EichstadtESE2017,
title = {Evaluation of dynamic measurement uncertainty – an open-source software package to bridge theory and practice},
journal = {J. Sens. Sens. Syst.},
year = {2017},
month = {2},
day = {14},
volume = {6},
pages = {97-105},
tags = {8.4,8.42,Unsicherheit,Dynamik},
DOI = {10.5194/jsss-6-97-2017},
author = {Eichst{\"a}dt, S and Elster, C and Smith, I M and Esward, T J}
}
@Article { BodnarLAPE2017,
title = {Bayesian estimation in random effects meta-analysis using a non-informative prior},
journal = {Statistics in Medicine},
year = {2017},
month = {2},
day = {1},
volume = {39},
number = {2},
pages = {378--399},
tags = {8.4,8.42,KC,Unsicherheit},
ISSN = {1097-0258},
DOI = {10.1002/sim.7156},
author = {Bodnar, O and Link, A and Arendack{\'a}, B and Possolo, A and Elster, C}
}
@Article { KlauenbergE2017,
title = {Sampling for assurance of future reliability},
journal = {Metrologia},
year = {2017},
month = {1},
day = {2},
volume = {54},
number = {1},
pages = {59--68},
keywords = {8.42, Unsicherheit},
tags = {8.42, Unsicherheit, Stichprobenverf},
DOI = {10.1088/1681-7575/54/1/59},
author = {Klauenberg, K and Elster, C}
}
@Article { WubbelerCE2016,
title = {Evaluation of uncertainties for CIELAB color coordinates},
journal = {Color Research \& Application},
year = {2016},
month = {12},
day = {31},
tags = {8.4,8.42,Unsicherheit},
DOI = {10.1002/col.22109},
author = {W{\"u}bbeler, G and Campos Acosta, J and Elster, C}
}
@Article { Bodnar2016b,
title = {Evaluation of uncertainty in the adjustment of fundamental constants},
journal = {Metrologia},
year = {2016},
month = {1},
day = {5},
volume = {53},
number = {1},
pages = {S46},
abstract = {Combining multiple measurement results for the same quantity is an important task in metrology and in many other areas. Examples include the determination of fundamental constants, the calculation of reference values in interlaboratory comparisons, or the meta-analysis of clinical studies. However, neither the GUM nor its supplements give any guidance for this task. Various approaches are applied such as weighted least-squares in conjunction with the Birge ratio or random effects models. While the former approach, which is based on a location-scale model, is particularly popular in metrology, the latter represents a standard tool used in statistics for meta-analysis. We investigate the reliability and robustness of the location-scale model and the random effects model with particular focus on resulting coverage or credible intervals. The interval estimates are obtained by adopting a Bayesian point of view in conjunction with a non-informative prior that is determined by a currently favored principle for selecting non-informative priors. Both approaches are compared by applying them to simulated data as well as to data for the Planck constant and the Newtonian constant of gravitation. Our results suggest that the proposed Bayesian inference based on the random effects model is more reliable and less sensitive to model misspecifications than the approach based on the location-scale model.},
tags = {8.42, Unsicherheit},
web_url = {http://stacks.iop.org/0026-1394/53/i=1/a=S46},
DOI = {10.1088/0026-1394/53/1/S46},
author = {Bodnar, O and Elster, C and Fischer, J and Possolo, A and Toman, B}
}
@Article { Klauenberg2016,
title = {Markov chain Monte Carlo methods: an introductory example},
journal = {Metrologia},
year = {2016},
month = {1},
day = {3},
volume = {53},
number = {1},
pages = {S32},
abstract = {When the Guide to the Expression of Uncertainty in Measurement (GUM) and methods from its supplements are not applicable, the Bayesian approach may be a valid and welcome alternative. Evaluating the posterior distribution, estimates or uncertainties involved in Bayesian inferences often requires numerical methods to avoid high-dimensional integrations. Markov chain Monte Carlo (MCMC) sampling is such a method---powerful, flexible and widely applied. Here, a concise introduction is given, illustrated by a simple, typical example from metrology. The Metropolis--Hastings algorithm is the most basic and yet flexible MCMC method. Its underlying concepts are explained and the algorithm is given step by step. The few lines of software code required for its implementation invite interested readers to get started. Diagnostics to evaluate the performance and common algorithmic choices are illustrated to calibrate the Metropolis--Hastings algorithm for efficiency. Routine application of MCMC algorithms may be hindered currently by the difficulty to assess the convergence of MCMC output and thus to assure the validity of results. An example points to the importance of convergence and initiates discussion about advantages as well as areas of research. Available software tools are mentioned throughout.},
keywords = {Bayesian, MCMC, Markov chain Monte Carlo},
tags = {8.42, Unsicherheit, Regression},
web_url = {http://stacks.iop.org/0026-1394/53/i=1/a=S32},
DOI = {10.1088/0026-1394/53/1/S32},
author = {Klauenberg, K and Elster, C}
}
@Article { Elster2016a,
title = {Bayesian regression versus application of least squares—an example},
journal = {Metrologia},
year = {2016},
month = {1},
day = {2},
volume = {53},
number = {1},
pages = {S10},
abstract = {Regression is an important task in metrology and least-squares methods are often applied in this context. Bayesian inference provides an alternative that can take into account available prior knowledge. We illustrate similarities and differences of the two approaches in terms of a particular nonlinear regression problem. The impact of prior knowledge utilized in the Bayesian regression depends on the amount of information contained in the data, and by considering data sets with different signal-to-noise ratios the relevance of the employed prior knowledge for the results is investigated. In addition, properties of the two approaches are explored in the context of the particular example.},
tags = {8.4, 8.42, Unsicherheit, Regression},
web_url = {http://stacks.iop.org/0026-1394/53/i=1/a=S10},
DOI = {10.1088/0026-1394/53/1/S10},
author = {Elster, C and W{\"u}bbeler, G}
}
@Article { Bodnar2015,
title = {Objective Bayesian Inference for a Generalized Marginal Random Effects Model},
journal = {Bayesian Analysis},
year = {2016},
month = {1},
day = {1},
volume = {11},
number = {1},
pages = {25-45},
note = {Open Access},
keywords = {objective Bayesian inference,random effects model,reference prior},
tags = {8.42, Unsicherheit},
web_url = {http://projecteuclid.org/euclid.ba/1423083638},
publisher = {International Society for Bayesian Analysis},
ISSN = {1931-6690},
DOI = {10.1214/14-BA933},
author = {Bodnar, O and Link, A and Elster, C}
}
@Article { Klauenberg2015_3,
title = {A tutorial on Bayesian Normal linear regression},
journal = {Metrologia},
year = {2015},
month = {1},
day = {7},
volume = {52},
number = {6},
pages = {878--892},
abstract = {Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view.Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach.These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.},
tags = {8.42, Regression, Unsicherheit},
DOI = {10.1088/0026-1394/52/6/878},
author = {Klauenberg, K and W{\"u}bbeler, G and Mickan, B and Harris, P and Elster, C}
}
@Techreport { NEW04_Bayes,
title = {A Guide to Bayesian Inference for Regression Problems},
year = {2015},
month = {1},
day = {6},
tags = {Regression, 8.42, Unsicherheit},
url = {fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/BPGWP1.pdf},
web_url = {http://www.ptb.de/emrp/new04.html},
institution = {EMRP NEW04},
author = {Elster, C and Klauenberg, K and Walzel, M and Harris, P M and Cox, M G and Matthews, C and Wright, L and Allard, A and Fischer, N and Ellison, S and Wilson, P and Pennecchi, F and Kok, G J P and Van der Veen, A and Pendrill, L}
}
@Article { Wubbeler2015,
title = {Explanatory power of degrees of equivalence in the presence of a random instability of the common measurand},
journal = {Metrologia},
year = {2015},
month = {1},
day = {3},
volume = {52},
number = {2},
pages = {400--405},
tags = {8.42, Unsicherheit, KC},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/52/2/400},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/52/2/400},
author = {W{\"u}bbeler, G and Bodnar, O and Mickan, B and Elster, C}
}
@Article { Kok2015,
title = {Bayesian analysis of a flow meter calibration problem},
journal = {Metrologia},
year = {2015},
month = {1},
day = {2},
volume = {52},
number = {2},
pages = {392-399},
tags = {Regression, 8.42, Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/52/2/392},
publisher = {IOP Publishing},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/52/2/392},
author = {Kok, G J P and van der Veen, A M H and Harris, P M and Smith, I M and Elster, C}
}
@Article { Possolo2014,
title = {Evaluating the uncertainty of input quantities in measurement models},
journal = {Metrologia},
year = {2014},
volume = {51},
number = {3},
pages = {339--353},
tags = {8.42,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/51/3/339},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/51/3/339},
author = {Possolo, A and Elster, C}
}
@Article { Bodnar2014a,
title = {Analytical derivation of the reference prior by sequential maximization of Shannon's mutual information in the multi-group parameter case},
journal = {Journal of Statistical Planning and Inference},
year = {2014},
volume = {147},
pages = {106--116},
abstract = {We provide an analytical derivation of a non-informative prior by sequential maximization of Shannon's mutual information in the multi-group parameter case assuming reasonable regularity conditions. We show that the derived prior coincides with the reference prior proposed by Berger and Bernardo, and that it can be considered as a useful alternative expression for the calculation of the reference prior. In using this expression we discuss the conditions under which an improper reference prior can be uniquely defined, i.e. when it does not depend on the particular choice of nested sequences of compact subsets of the parameter space needed for its construction. We also present the conditions under which the reference prior coincides with Jeffreys' prior.},
keywords = {Bayes,Reference prior,Shannon's mutual information,statistics},
tags = {8.42, Unsicherheit},
web_url = {http://www.sciencedirect.com/science/article/pii/S0378375813002802},
ISSN = {03783758},
DOI = {10.1016/j.jspi.2013.11.003},
author = {Bodnar, O and Elster, C}
}
@Article { Bodnar2014,
title = {On the adjustment of inconsistent data using the Birge ratio},
journal = {Metrologia},
year = {2014},
volume = {51},
number = {5},
pages = {516--521},
tags = {8.42,KC,Regression, Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/51/5/516},
publisher = {IOP Publishing},
language = {en},
ISBN = {doi:10.1088/0026-1394/51/5/516},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/51/5/516},
author = {Bodnar, O and Elster, C}
}
@Article { Arendacka2014a,
title = {Linear Mixed Models: Gum and Beyond},
journal = {Measurement Science Review},
year = {2014},
volume = {14},
number = {2},
pages = {52-61},
abstract = {In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneļ¬cial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.},
keywords = {dynamic measurement, acceleration, dynamic calibration, mixed model, design of experiment},
tags = {8.42, Dynamik, Unsicherheit},
url = {fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/epjconf_icm2014_00003.pdf},
web_url = {http://www.degruyter.com/view/j/msr.2014.14.issue-2/msr-2014-0009/msr-2014-0009.xml},
ISSN = {1335-8871},
DOI = {10.2478/msr-2014-0009},
author = {Arendack{\'a}, B and T{\"a}ubner, A and Eichst{\"a}dt, S and Bruns, T and Elster, C}
}
@Article { Haslett2014,
title = {The link between the mixed and fixed linear models revisited},
journal = {Statistical Papers},
year = {2014},
volume = {56},
number = {3},
pages = {849--861},
keywords = {mixed linear models,statistics},
tags = {8.42, Unsicherheit},
web_url = {http://link.springer.com/10.1007/s00362-014-0611-9},
ISSN = {0932-5026},
DOI = {10.1007/s00362-014-0611-9},
author = {Haslett, S J and Puntanen, S and Arendack{\'a}, B}
}
@Article { Elster2014,
title = {Bayesian uncertainty analysis compared with the application of the GUM and its supplements},
journal = {Metrologia},
year = {2014},
volume = {51},
number = {4},
pages = {S159--S166},
tags = {8.42, Bayesian, Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/51/4/S159},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/51/4/S159},
author = {Elster, C}
}
@Article { Wubbeler2013,
title = {Simplified evaluation of magnetic field fluctuation thermometry},
journal = {Measurement Science and Technology},
year = {2013},
volume = {24},
number = {11},
pages = {115004},
tags = {8.42,Bayes,MFFT,Regression, Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0957-0233/24/11/115004},
publisher = {IOP Publishing},
language = {en},
ISSN = {0957-0233},
DOI = {10.1088/0957-0233/24/11/115004},
author = {W{\"u}bbeler, G and Elster, C}
}
@Article { Eichstaedt2012a,
title = {Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements},
journal = {Metrologia},
year = {2012},
volume = {49},
number = {3},
pages = {401},
abstract = {Measurement of quantities having time-dependent values such as force, acceleration or pressure is a topic of growing importance in metrology. The application of the Guide to the Expression of Uncertainty in Measurement (GUM) and its Supplements to the evaluation of uncertainty for such quantities is challenging. We address the efficient implementation of the Monte Carlo method described in GUM Supplements 1 and 2 for this task. The starting point is a time-domain observation equation. The steps of deriving a corresponding measurement model, the assignment of probability distributions to the input quantities in the model, and the propagation of the distributions through the model are all considered. A direct implementation of a Monte Carlo method can be intractable on many computers since the storage requirement of the method can be large compared with the available computer memory. Two memory-efficient alternatives to the direct implementation are proposed. One approach is based on applying updating formulae for calculating means, variances and point-wise histograms. The second approach is based on evaluating the measurement model sequentially in time. A simulated example is used to compare the performance of the direct and alternative procedures.},
tags = {8.42, Dynamik, Unsicherheit},
DOI = {10.1088/0026-1394/49/3/401},
author = {Eichst{\"a}dt, S and Link, A and Harris, P M and Elster, C}
}
@Article { Klauenberg2012,
title = {The multivariate normal mean - sensitivity of its objective Bayesian estimates},
journal = {Metrologia},
year = {2012},
volume = {49},
number = {3},
pages = {395--400},
tags = {8.42,Bayes,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/49/3/395},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/49/3/395},
author = {Klauenberg, K and Elster, C}
}
@Article { Wubbeler2012,
title = {Analysis of magnetic field fluctuation thermometry using Bayesian inference},
journal = {Measurement Science and Technology},
year = {2012},
volume = {23},
number = {12},
pages = {125004},
tags = {8.42,Bayes,MFFT,Regression, Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0957-0233/23/12/125004},
publisher = {IOP Publishing},
language = {en},
ISSN = {0957-0233},
DOI = {10.1088/0957-0233/23/12/125004},
author = {W{\"u}bbeler, G and Schm{\"a}hling, F and Beyer, J and Engert, J and Elster, C}
}
@Article { Bich2012,
title = {Revision of the ''Guide to the Expression of Uncertainty in Measurement''},
journal = {Metrologia},
year = {2012},
volume = {49},
number = {6},
pages = {702--705},
tags = {8.42,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/49/6/702},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/49/6/702},
author = {Bich, W and Cox, M G and Dybkaer, R and Elster, C and Estler, W T and Hibbert, B and Imai, H and Kool, W and Michotte, C and Nielsen, L and Pendrill, L and Sidney, S and van der Veen, A M H and W{\"o}ger, W}
}
@Article { Elster2012c,
title = {On the choice of a noninformative prior for Bayesian inference of discretized normal observations},
journal = {Computational Statistics},
year = {2012},
volume = {27},
number = {2},
pages = {219--235},
tags = {8.42,Bayes,Unsicherheit},
web_url = {http://link.springer.com/10.1007/s00180-011-0251-7},
ISSN = {0943-4062},
DOI = {10.1007/s00180-011-0251-7},
author = {Elster, C and Lira, I}
}
@Inbook { Wuebbeler2012c,
title = {Assessment of the GUM S1 Adaptive Monte Carlo Scheme},
year = {2012},
volume = {Advanced Mathematical \& Computational Tools in Metrology IX},
pages = {434},
tags = {8.42, Unsicherheit},
editor = {F. Pavese, M. B{\"a}r, J.M. Limares, C. Perruchet, N.F. Zhang},
publisher = {World Scientific New Jersey},
series = {Series on Advances in Mathematics for Applied Sciences},
edition = {84},
chapter = {54},
author = {W{\"u}bbeler, G and Harris, P M and Cox, M G and Elster, C}
}
@Inbook { Eichstaedt2012e,
title = {Uncertainty evaluation for continuous-time measurements},
year = {2012},
volume = {Advanced Mathematical \& Computational Tools in Metrology and Testing IX },
pages = {126-135},
keywords = {dynamic measurement, continuous function, stochastic process, uncertainty},
tags = {8.42, Dynamik, Unsicherheit},
editor = {F. Pavese, M. B{\"a}r, J.-R. Filtz, A. B. Forbes, L. Pendrill, K. Shirono},
publisher = {World Scientific New Jersey},
series = {Series on Advances in Mathematics for Applied Sciences},
edition = {84},
chapter = {16},
author = {Eichst{\"a}dt, S and Elster, C}
}
@Inbook { Esward2012,
title = {Uncertainty evaluation for traceable dynamic measurement of mechanical quantities: A case study in dynamic pressure calibration},
year = {2012},
volume = {Advanced Mathematical \& Computational Tools in Metrology and Testing IX },
pages = {143-151},
keywords = {dynamic pressure, calibration, dynamic measurement},
tags = {8.42, Dynamik, Unsicherheit},
editor = {F. Pavese, M. B{\"a}r, J.-R. Filtz, A. B. Forbes, L. Pendrill, K. Shirono},
publisher = {World Scientific New Jersey},
series = {Series on Advances in Mathematics for Applied Sciences},
edition = {84},
chapter = {19},
author = {Esward, T J and Matthews, C and Downes, S and Knott, A and Eichst{\"a}dt, S and Elster, C}
}
@Article { Bodnar2011,
title = {On the application of Supplement 1 to the GUM to non-linear problems},
journal = {Metrologia},
year = {2011},
volume = {48},
number = {5},
pages = {333--342},
tags = {8.42,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/48/5/014},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/48/5/014},
author = {Bodnar, O and W{\"u}bbeler, G and Elster, C}
}
@Article { Elster2011a,
title = {Bayesian uncertainty analysis for a regression model versus application of GUM Supplement 1 to the least-squares estimate},
journal = {Metrologia},
year = {2011},
volume = {48},
number = {5},
pages = {233--240},
tags = {8.42, Regression, Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/48/5/001},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/48/5/001},
author = {Elster, C and Toman, B}
}
@Article { Wubbeler2010,
title = {A two-stage procedure for determining the number of trials in the application of a Monte Carlo method for uncertainty evaluation},
journal = {Metrologia},
year = {2010},
volume = {47},
number = {3},
pages = {317--324},
tags = {8.42,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/47/3/023},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/47/3/023},
author = {W{\"u}bbeler, G and Harris, P M and Cox, M G and Elster, C}
}
@Article { Eichstadt2010k,
title = {On-line dynamic error compensation of accelerometers by uncertainty-optimal filtering},
journal = {Measurement},
year = {2010},
volume = {43},
number = {5},
pages = {708-713},
abstract = {The output signal of an accelerometer typically contains dynamic errors when a broadband acceleration is applied. In order to determine the applied acceleration, post-processing of the accelerometer{\^a}{\euro}™s output signal is required. To this end, we propose the application of a digital FIR filter. We evaluate the uncertainty associated with the filtered output signal and give explicit formulae which allow for on-line calculation. In this way, estimation of the applied acceleration and the calculation of associated uncertainties may be carried out during the measurement. The resulting uncertainties can strongly depend on the design of the applied filter and we describe a simple method to construct an uncertainty-optimal filter. The benefit of the proposed procedures is illustrated by means of simulated measurements.},
keywords = {Accelerometer,Digital filter,Dynamic measurements,Dynamik,Uncertainty},
tags = {8.42, Dynamik, Unsicherheit},
web_url = {http://www.sciencedirect.com/science/article/pii/S0263224110000023},
DOI = {10.1016/j.measurement.2009.12.028},
author = {Eichst{\"a}dt, S and Link, A and Bruns, T and Elster, C}
}
@Article { Link2009b,
title = {Uncertainty evaluation for IIR (infinite impulse response) filtering using a state-space approach},
journal = {Measurement Science and Technology},
year = {2009},
volume = {20},
number = {5},
pages = {055104},
keywords = {dynamic measurement, digital filter, deconvolution, dynamic uncertainty},
tags = {8.42,Dynamik, Unsicherheit},
publisher = {IOP Publishing},
DOI = {10.1088/0957-0233/20/5/055104},
author = {Link, A and Elster, C}
}
@Article { Elster2009,
title = {Bayesian uncertainty analysis under prior ignorance of the measurand versus analysis using the Supplement 1 to the Guide : a comparison},
journal = {Metrologia},
year = {2009},
volume = {46},
number = {3},
pages = {261--266},
tags = {8.42,Bayes,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/46/3/013},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/46/3/013},
author = {Elster, C and Toman, B}
}
@Inbook { Lira2009,
title = {Derivation of an output PDF from Bayes theorem and the principle of maximum entropy},
year = {2009},
volume = {Advanced Mathematical \& Computational Tools in Metrology VIII},
pages = {213},
tags = {8.42, Unsicherheit},
editor = {F. Pavese, M. B{\"a}r, J.M. Limares, C. Perruchet, N.F. Zhang},
publisher = {World Scientific New Jersey},
series = {Series on Advances in Mathematics for Applied Sciences},
edition = {78},
chapter = {31},
author = {Lira, I and Elster, C and W{\"o}ger, W and Cox, M G}
}
@Inbook { Wuebbeler2009,
title = {Impact of correlation in the measured frequency response on the results of a dynamic calibration},
year = {2009},
volume = {Advanced Mathematical \& Computational Tools in Metrology VIII},
pages = {369-374},
keywords = {dynamic measurement, frequency response, dynamic calibration},
tags = {8.42, Dynamik, Unsicherheit},
editor = {F. Pavese, M. B{\"a}r, J.M. Limares, C. Perruchet, N.F. Zhang},
publisher = {World Scientific New Jersey},
series = {Series on Advances in Mathematics for Applied Sciences},
edition = {78},
chapter = {52},
author = {W{\"u}bbeler, G and Link, A and Bruns, T and Elster, C}
}
@Inbook { Elster2009m,
title = {Analysis of dynamic measurements: compensation of dynamic error and evaluation of uncertainty},
year = {2009},
volume = {Advanced Mathematical \& Computational Tools in Metrology VIII},
pages = {80-89},
tags = {8.42, Dynamik, Unsicherheit},
editor = {F. Pavese, M. B{\"a}r, J.M. Limares, C. Perruchet, N.F. Zhang},
publisher = {World Scientific New Jersey},
series = {Series on Advances in Mathematics for Applied Sciences},
edition = {78},
chapter = {13},
author = {Elster, C and Link, A}
}
@Article { Wubbeler2008,
title = {Evaluation of measurement uncertainty and its numerical calculation by a Monte Carlo method},
journal = {Measurement Science and Technology},
year = {2008},
volume = {19},
number = {8},
pages = {084009},
tags = {8.42,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0957-0233/19/8/084009},
publisher = {IOP Publishing},
language = {en},
ISSN = {0957-0233},
DOI = {10.1088/0957-0233/19/8/084009},
author = {W{\"u}bbeler, G and Krystek, M and Elster, C}
}
@Article { Elster2008c,
title = {Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system},
journal = {Metrologia},
year = {2008},
volume = {45},
number = {4},
pages = {464-473},
keywords = {dynamic measurement, digital filter, deconvolution, dynamic uncertainty},
tags = {8.42,Dynamik, Unsicherheit},
publisher = {IOP Publishing},
DOI = {10.1088/0026-1394/45/4/013},
author = {Elster, C and Link, A}
}
@Article { Lira2007,
title = {Probabilistic and least-squares inference of the parameters of a straight-line model},
journal = {Metrologia},
year = {2007},
volume = {44},
number = {5},
pages = {379--384},
tags = {8.42,Bayes,Regression,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/44/5/014},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/44/5/014},
author = {Lira, I and Elster, C and W{\"o}ger, W}
}
@Article { Elster2007,
title = {Draft GUM Supplement 1 and Bayesian analysis},
journal = {Metrologia},
year = {2007},
volume = {44},
number = {3},
pages = {L31--L32},
tags = {8.42,Bayes,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/44/3/N03},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/44/3/N03},
author = {Elster, C and W{\"o}ger, W and Cox, M G}
}
@Article { Elster2007b,
title = {Analysis of dynamic measurements and determination of time-dependent measurement uncertainty using a second-order model},
journal = {Measurement Science and Technology},
year = {2007},
volume = {18},
number = {12},
pages = {3682-3687},
keywords = {dynamic measurement},
tags = {8.42,Dynamik, Unsicherheit},
publisher = {IOP Publishing},
language = {en},
DOI = {10.1088/0957-0233/18/12/002},
author = {Elster, C and Link, A and Bruns, T}
}
@Article { Elster2007a,
title = {Calculation of uncertainty in the presence of prior knowledge},
journal = {Metrologia},
year = {2007},
volume = {44},
number = {2},
pages = {111--116},
tags = {8.42,Unsicherheit},
web_url = {http://iopscience.iop.org/article/10.1088/0026-1394/44/2/002},
publisher = {IOP Publishing},
language = {en},
ISSN = {0026-1394},
DOI = {10.1088/0026-1394/44/2/002},
author = {Elster, C}
}
@Article { Elster2005a,
title = {Quantitative magnetic resonance spectroscopy: semi-parametric modeling and determination of uncertainties},
journal = {Magnetic resonance in medicine},
year = {2005},
volume = {53},
number = {6},
pages = {1288--96},
abstract = {A semi-parametric approach for the quantitative analysis of magnetic resonance (MR) spectra is proposed and an uncertainty analysis is given. Single resonances are described by parametric models or by parametrized in vitro spectra and the baseline is determined nonparametrically by regularization. By viewing baseline estimation in a reproducing kernel Hilbert space, an explicit parametric solution for the baseline is derived. A Bayesian point of view is adopted to derive uncertainties, and the many parameters associated with the baseline solution are treated as nuisance parameters. The derived uncertainties formally reduce to Cram{{\'e}}r-Rao lower bounds for the parametric part of the model in the case of a vanishing baseline. The proposed uncertainty calculation was applied to simulated and measured MR spectra and the results were compared to Cram{{\'e}}r-Rao lower bounds derived after the nonparametrically estimated baselines were subtracted from the spectra. In particular, for high SNR and strong baseline contributions the proposed procedure yields a more appropriate characterization of the accuracy of parameter estimates than Cr{{\'e}}mer-Rao lower bounds, which tend to overestimate accuracy.},
keywords = {Bayes Theorem,Brain Chemistry,Computer Simulation,Computer-Assisted,Humans,Least-Squares Analysis,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: methods,Models, Statistical,Regression,Signal Processing, Computer-Assisted,Statistical},
tags = {8.42, Unsicherheit, in-vivo},
web_url = {http://www.ncbi.nlm.nih.gov/pubmed/15906296},
ISSN = {0740-3194},
DOI = {10.1002/mrm.20500},
author = {Elster, C and Schubert, F and Link, A and Walzel, M and Seifert, F and Rinneberg, H}
}