This file was created by the TYPO3 extension
bib
--- Timezone: CET
Creation date: 2022-11-29
Creation time: 08-36-48
--- Number of references
64
article
KokWE2022
Impact of Imperfect Artefacts and the Modus Operandi on
Uncertainty Quantification Using Virtual Instruments
Metrology
2022
6
12
2
311--319
8.4,8.42,Messunsicherheit,Form
10.3390/metrology2020019
GKok
GWübbeler
CElster
article
MarschallSSE2022
Uncertainty propagation in quantitative magnetic force microscopy using a Monte-Carlo method
IEEE Transactions on Magnetics
2022
2
21
1--1
8.4,8.42,Messunsicherheit
10.1109/TMAG.2022.3153176
MMarschall
SSievers
H WSchumacher
CElster
article
MarschallWE2022
Rejection sampling for Bayesian uncertainty evaluation using the Monte Carlo techniques of GUM-S1
Metrologia
2022
2
1
59
1
015004
8.4,8.42,Messunsicherheit
10.1088/1681-7575/ac3920
MMarschall
GWübbeler
CElster
article
GruberDSEE2022
Discrete wavelet transform on uncertain data: Efficient online implementation for practical applications.
Advanced Mathematical and Computational Tools in Metrology and Testing XII, Series on Advances in Mathematics for Applied Sciences
2022
1
30
90
8.4,8.42,Messunsicherheit
978-981-1242-37-3
MGruber
TDorst
ASchütze
SEichstädt
CElster
phdthesis
Metzner2021
Bayesian data analysis for magnetic resonance fingerprinting
2021
12
7
publiziert
8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna
TU Berlin
PhD Thesis
10.14279/depositonce-12455
SMetzner
article
MartinE2021
GUI for Bayesian sample size planning in type A uncertainty evaluation
Measurement Science and Technology
2021
4
30
32
7
5005
8.4,8.42,Messunsicherheit
10.1088/1361-6501/abe2bd
JMartin
CElster
article
MetznerWFGKE2021
Bayesian uncertainty quantification for magnetic resonance fingerprinting
Physics in Medicine & Biology
2021
3
1
66
7
075006
8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna
10.1088/1361-6560/abeae7
SMetzner
GWübbeler
SFlassbeck
CGatefait
CKolbitsch
CElster
article
WubbelerME2020
A simple method for Bayesian uncertainty evaluation in linear models
Metrologia
2020
10
21
57
6
065010
8.4,8.42,Unsicherheit
10.1088/1681-7575/aba3b8
GWübbeler
MMarschall
CElster
article
MartinE2020_2
The variation of the posterior variance and Bayesian sample size determination
Statistical Methods & Applications
2020
8
25
1613-981X
8.4,8.42,Unsicherheit
10.1007/s10260-020-00545-3
JMartin
CElster
article
DemeyerFE2020
Guidance on Bayesian uncertainty evaluation for a class of GUM measurement models
Metrologia
2020
8
18
8.4,8.42,Unsicherheit
10.1088/1681-7575/abb065
SDemeyer
NFischer
CElster
article
BartlEMSVW2020
Thermal expansion and compressibility of single-crystal silicon between 285 K and 320 K
Measurement Science and Technology
2020
4
3
31
6
8.4,8.42,Unsicherheit,Regression
10.1088/1361-6501/ab7359
GBartl
CElster
JMartin
RSchödel
MVoigt
AWalkov
article
WubbelerE2020
On the transferability of the GUM-S1 type A uncertainty
Metrologia
2020
1
23
57
1
8.4,8.42,Unsicherheit
10.1088/1681-7575/ab50d6
GWübbeler
CElster
article
KlauenbergWE2019
About not Correcting for Systematic Effects
Measurement Science Review
2019
9
30
19
5
204--208
8.4,8.42,Unsicherheit
10.2478/msr-2019-0026
KKlauenberg
GWübbeler
CElster
article
MetznerWE2018
Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging
AStA Adv Stat Anal
2019
8
29
103
3
333--355
8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna
10.1007/s10182-018-00334-0
SMetzner
GWübbeler
CElster
article
MartinBE2019
Application of Bayesian model averaging to the determination of thermal expansion of single-crystal silicon
Measurement Science and Technology
2019
2
21
30
045012
8.4,8.42,Unsicherheit,Regression
10.1088/1361-6501/ab094b
JMartin
GBartl
CElster
article
BodnarE2016
Assessment of vague and noninformative priors for Bayesian estimation of the realized random effects in random-effects meta-analysis
AStA Advances in Statistical Analysis
2018
1
31
102
1
1--20
8.42,KC,Unsicherheit
10.1007/s10182-016-0279-7
OBodnar
CElster
article
WubbelerBE2018
Robust Bayesian linear regression with application to an analysis of the CODATA values for the Planck constant
Metrologia
2018
1
2
55
1
20
8.4,8.42,Unsicherheit,Regression
10.1088/1681-7575/aa98aa
GWübbeler
OBodnar
CElster
article
SchmahlingWKRSTSE2017
Uncertainty evaluation and propagation for spectral measurements
Color, Research and Application
2017
9
26
43
1
6--16
8.4,8.42,Unsicherheit,LargeScaleDataAna
10.1002/col.22185
FSchmähling
GWübbeler
UKrüger
BRuggaber
FSchmidt
R DTaubert
ASperling
CElster
article
ReginattoAE2017
Assessment of CT image quality using a Bayesian approach
Metrologia
2017
6
14
54
4
S74--S82
8.4,8.42,Unsicherheit
10.1088/1681-7575/aa735b
MReginatto
MAnton
CElster
article
EichstadtW2017
Evaluation of uncertainty for regularized deconvolution: A case study in hydrophone measurements
J. Acoust. Soc. Am.
2017
6
6
141
6
4155--4167
8.4,8.42,Unsicherheit,Dynamik
10.1121/1.4983827
SEichstädt
VWilkens
article
BodnarBE2017
Bayesian inference for measurements of ionizing radiation under partial information
Metrologia
2017
5
11
54
3
S29--S33
8.4,8.42,Unsicherheit
10.1088/1681-7575/aa69ad
OBodnar
RBehrens
CElster
article
EichstadtESE2017
Evaluation of dynamic measurement uncertainty – an open-source software package to bridge theory and practice
J. Sens. Sens. Syst.
2017
2
14
6
97-105
8.4,8.42,Unsicherheit,Dynamik
10.5194/jsss-6-97-2017
SEichstädt
CElster
I MSmith
T JEsward
article
BodnarLAPE2017
Bayesian estimation in random effects meta-analysis using a non-informative prior
Statistics in Medicine
2017
2
1
39
2
378--399
8.4,8.42,KC,Unsicherheit
1097-0258
10.1002/sim.7156
OBodnar
ALink
BArendacká
APossolo
CElster
article
KlauenbergE2017
Sampling for assurance of future reliability
Metrologia
2017
1
2
54
1
59--68
8.42, Unsicherheit
8.42, Unsicherheit, Stichprobenverf
10.1088/1681-7575/54/1/59
KKlauenberg
CElster
article
WubbelerCE2016
Evaluation of uncertainties for CIELAB color coordinates
Color Research & Application
2016
12
31
8.4,8.42,Unsicherheit
10.1002/col.22109
GWübbeler
JCampos Acosta
CElster
article
Bodnar2016b
Evaluation of uncertainty in the adjustment of fundamental constants
Metrologia
2016
1
5
53
1
S46
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.
8.42, Unsicherheit
http://stacks.iop.org/0026-1394/53/i=1/a=S46
10.1088/0026-1394/53/1/S46
OBodnar
CElster
JFischer
APossolo
BToman
article
Klauenberg2016
Markov chain Monte Carlo methods: an introductory example
Metrologia
2016
1
3
53
1
S32
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.
Bayesian, MCMC, Markov chain Monte Carlo
8.42, Unsicherheit, Regression
http://stacks.iop.org/0026-1394/53/i=1/a=S32
10.1088/0026-1394/53/1/S32
KKlauenberg
CElster
article
Elster2016a
Bayesian regression versus application of least squares—an example
Metrologia
2016
1
2
53
1
S10
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.
8.4, 8.42, Unsicherheit, Regression
http://stacks.iop.org/0026-1394/53/i=1/a=S10
10.1088/0026-1394/53/1/S10
CElster
GWübbeler
article
Bodnar2015
Objective Bayesian Inference for a Generalized Marginal Random Effects Model
Bayesian Analysis
2016
1
1
11
1
25-45
Open Access
objective Bayesian inference,random effects model,reference prior
8.42, Unsicherheit
http://projecteuclid.org/euclid.ba/1423083638
International Society for Bayesian Analysis
1931-6690
10.1214/14-BA933
OBodnar
ALink
CElster
article
Klauenberg2015_3
A tutorial on Bayesian Normal linear regression
Metrologia
2015
1
7
52
6
878--892
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.
8.42, Regression, Unsicherheit
10.1088/0026-1394/52/6/878
KKlauenberg
GWübbeler
BMickan
PHarris
CElster
techreport
NEW04_Bayes
A Guide to Bayesian Inference for Regression Problems
2015
1
6
Regression, 8.42, Unsicherheit
fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/BPGWP1.pdf
http://www.ptb.de/emrp/new04.html
EMRP NEW04
CElster
KKlauenberg
MWalzel
P MHarris
M GCox
CMatthews
LWright
AAllard
NFischer
SEllison
PWilson
FPennecchi
G J PKok
AVan der Veen
LPendrill
article
Wubbeler2015
Explanatory power of degrees of equivalence in the presence of a random instability of the common measurand
Metrologia
2015
1
3
52
2
400--405
8.42, Unsicherheit, KC
http://iopscience.iop.org/article/10.1088/0026-1394/52/2/400
IOP Publishing
en
0026-1394
10.1088/0026-1394/52/2/400
GWübbeler
OBodnar
BMickan
CElster
article
Kok2015
Bayesian analysis of a flow meter calibration problem
Metrologia
2015
1
2
52
2
392-399
Regression, 8.42, Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/52/2/392
IOP Publishing
0026-1394
10.1088/0026-1394/52/2/392
G J PKok
A M Hvan der Veen
P MHarris
I MSmith
CElster
article
Possolo2014
Evaluating the uncertainty of input quantities in measurement models
Metrologia
2014
51
3
339--353
8.42,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/51/3/339
IOP Publishing
en
0026-1394
10.1088/0026-1394/51/3/339
APossolo
CElster
article
Bodnar2014a
Analytical derivation of the reference prior by sequential maximization of Shannon's mutual information in the multi-group parameter case
Journal of Statistical Planning and Inference
2014
147
106--116
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.
Bayes,Reference prior,Shannon's mutual information,statistics
8.42, Unsicherheit
http://www.sciencedirect.com/science/article/pii/S0378375813002802
03783758
10.1016/j.jspi.2013.11.003
OBodnar
CElster
article
Bodnar2014
On the adjustment of inconsistent data using the Birge ratio
Metrologia
2014
51
5
516--521
8.42,KC,Regression, Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/51/5/516
IOP Publishing
en
doi:10.1088/0026-1394/51/5/516
0026-1394
10.1088/0026-1394/51/5/516
OBodnar
CElster
article
Arendacka2014a
Linear Mixed Models: Gum and Beyond
Measurement Science Review
2014
14
2
52-61
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 beneﬁcial 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.
dynamic measurement, acceleration, dynamic calibration, mixed model, design of experiment
8.42, Dynamik, Unsicherheit
fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/epjconf_icm2014_00003.pdf
http://www.degruyter.com/view/j/msr.2014.14.issue-2/msr-2014-0009/msr-2014-0009.xml
1335-8871
10.2478/msr-2014-0009
BArendacká
ATäubner
SEichstädt
TBruns
CElster
article
Haslett2014
The link between the mixed and fixed linear models revisited
Statistical Papers
2014
56
3
849--861
mixed linear models,statistics
8.42, Unsicherheit
http://link.springer.com/10.1007/s00362-014-0611-9
0932-5026
10.1007/s00362-014-0611-9
S JHaslett
SPuntanen
BArendacká
article
Elster2014
Bayesian uncertainty analysis compared with the application of the GUM and its supplements
Metrologia
2014
51
4
S159--S166
8.42, Bayesian, Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/51/4/S159
IOP Publishing
en
0026-1394
10.1088/0026-1394/51/4/S159
CElster
article
Wubbeler2013
Simplified evaluation of magnetic field fluctuation thermometry
Measurement Science and Technology
2013
24
11
115004
8.42,Bayes,MFFT,Regression, Unsicherheit
http://iopscience.iop.org/article/10.1088/0957-0233/24/11/115004
IOP Publishing
en
0957-0233
10.1088/0957-0233/24/11/115004
GWübbeler
CElster
article
Eichstaedt2012a
Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements
Metrologia
2012
49
3
401
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.
8.42, Dynamik, Unsicherheit
10.1088/0026-1394/49/3/401
SEichstädt
ALink
P MHarris
CElster
article
Klauenberg2012
The multivariate normal mean - sensitivity of its objective Bayesian estimates
Metrologia
2012
49
3
395--400
8.42,Bayes,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/49/3/395
IOP Publishing
en
0026-1394
10.1088/0026-1394/49/3/395
KKlauenberg
CElster
article
Wubbeler2012
Analysis of magnetic field fluctuation thermometry using Bayesian inference
Measurement Science and Technology
2012
23
12
125004
8.42,Bayes,MFFT,Regression, Unsicherheit
http://iopscience.iop.org/article/10.1088/0957-0233/23/12/125004
IOP Publishing
en
0957-0233
10.1088/0957-0233/23/12/125004
GWübbeler
FSchmähling
JBeyer
JEngert
CElster
article
Bich2012
Revision of the "Guide to the Expression of Uncertainty in Measurement"
Metrologia
2012
49
6
702--705
8.42,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/49/6/702
IOP Publishing
en
0026-1394
10.1088/0026-1394/49/6/702
WBich
M GCox
RDybkaer
CElster
W TEstler
BHibbert
HImai
WKool
CMichotte
LNielsen
LPendrill
SSidney
A M Hvan der Veen
WWöger
article
Elster2012c
On the choice of a noninformative prior for Bayesian inference of discretized normal observations
Computational Statistics
2012
27
2
219--235
8.42,Bayes,Unsicherheit
http://link.springer.com/10.1007/s00180-011-0251-7
0943-4062
10.1007/s00180-011-0251-7
CElster
ILira
inbook
Wuebbeler2012c
Assessment of the GUM S1 Adaptive Monte Carlo Scheme
2012
Advanced Mathematical & Computational Tools in Metrology IX
434
8.42, Unsicherheit
F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang
World Scientific New Jersey
Series on Advances in Mathematics for Applied Sciences
84
54
GWübbeler
P MHarris
M GCox
CElster
inbook
Eichstaedt2012e
Uncertainty evaluation for continuous-time measurements
2012
Advanced Mathematical & Computational Tools in Metrology and Testing IX
126-135
dynamic measurement, continuous function, stochastic process, uncertainty
8.42, Dynamik, Unsicherheit
F. Pavese, M. Bär, J.-R. Filtz, A. B. Forbes, L. Pendrill, K. Shirono
World Scientific New Jersey
Series on Advances in Mathematics for Applied Sciences
84
16
SEichstädt
CElster
inbook
Esward2012
Uncertainty evaluation for traceable dynamic measurement of mechanical quantities: A case study in dynamic pressure calibration
2012
Advanced Mathematical & Computational Tools in Metrology and Testing IX
143-151
dynamic pressure, calibration, dynamic measurement
8.42, Dynamik, Unsicherheit
F. Pavese, M. Bär, J.-R. Filtz, A. B. Forbes, L. Pendrill, K. Shirono
World Scientific New Jersey
Series on Advances in Mathematics for Applied Sciences
84
19
T JEsward
CMatthews
SDownes
AKnott
SEichstädt
CElster
article
Bodnar2011
On the application of Supplement 1 to the GUM to non-linear problems
Metrologia
2011
48
5
333--342
8.42,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/48/5/014
IOP Publishing
en
0026-1394
10.1088/0026-1394/48/5/014
OBodnar
GWübbeler
CElster
article
Elster2011a
Bayesian uncertainty analysis for a regression model versus application of GUM Supplement 1 to the least-squares estimate
Metrologia
2011
48
5
233--240
8.42, Regression, Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/48/5/001
IOP Publishing
en
0026-1394
10.1088/0026-1394/48/5/001
CElster
BToman
article
Wubbeler2010
A two-stage procedure for determining the number of trials in the application of a Monte Carlo method for uncertainty evaluation
Metrologia
2010
47
3
317--324
8.42,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/47/3/023
IOP Publishing
en
0026-1394
10.1088/0026-1394/47/3/023
GWübbeler
P MHarris
M GCox
CElster
article
Eichstadt2010k
On-line dynamic error compensation of accelerometers by uncertainty-optimal filtering
Measurement
2010
43
5
708-713
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 accelerometerâ€™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.
Accelerometer,Digital filter,Dynamic measurements,Dynamik,Uncertainty
8.42, Dynamik, Unsicherheit
http://www.sciencedirect.com/science/article/pii/S0263224110000023
10.1016/j.measurement.2009.12.028
SEichstädt
ALink
TBruns
CElster
article
Link2009b
Uncertainty evaluation for IIR (infinite impulse response) filtering using a state-space approach
Measurement Science and Technology
2009
20
5
055104
dynamic measurement, digital filter, deconvolution, dynamic uncertainty
8.42,Dynamik, Unsicherheit
IOP Publishing
10.1088/0957-0233/20/5/055104
ALink
CElster
article
Elster2009
Bayesian uncertainty analysis under prior ignorance of the measurand versus analysis using the Supplement 1 to the Guide : a comparison
Metrologia
2009
46
3
261--266
8.42,Bayes,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/46/3/013
IOP Publishing
en
0026-1394
10.1088/0026-1394/46/3/013
CElster
BToman
inbook
Lira2009
Derivation of an output PDF from Bayes theorem and the principle of maximum entropy
2009
Advanced Mathematical & Computational Tools in Metrology VIII
213
8.42, Unsicherheit
F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang
World Scientific New Jersey
Series on Advances in Mathematics for Applied Sciences
78
31
ILira
CElster
WWöger
M GCox
inbook
Wuebbeler2009
Impact of correlation in the measured frequency response on the results of a dynamic calibration
2009
Advanced Mathematical & Computational Tools in Metrology VIII
369-374
dynamic measurement, frequency response, dynamic calibration
8.42, Dynamik, Unsicherheit
F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang
World Scientific New Jersey
Series on Advances in Mathematics for Applied Sciences
78
52
GWübbeler
ALink
TBruns
CElster
inbook
Elster2009m
Analysis of dynamic measurements: compensation of dynamic error and evaluation of uncertainty
2009
Advanced Mathematical & Computational Tools in Metrology VIII
80-89
8.42, Dynamik, Unsicherheit
F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang
World Scientific New Jersey
Series on Advances in Mathematics for Applied Sciences
78
13
CElster
ALink
article
Wubbeler2008
Evaluation of measurement uncertainty and its numerical calculation by a Monte Carlo method
Measurement Science and Technology
2008
19
8
084009
8.42,Unsicherheit
http://iopscience.iop.org/article/10.1088/0957-0233/19/8/084009
IOP Publishing
en
0957-0233
10.1088/0957-0233/19/8/084009
GWübbeler
MKrystek
CElster
article
Elster2008c
Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system
Metrologia
2008
45
4
464-473
dynamic measurement, digital filter, deconvolution, dynamic uncertainty
8.42,Dynamik, Unsicherheit
IOP Publishing
10.1088/0026-1394/45/4/013
CElster
ALink
article
Lira2007
Probabilistic and least-squares inference of the parameters of a straight-line model
Metrologia
2007
44
5
379--384
8.42,Bayes,Regression,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/44/5/014
IOP Publishing
en
0026-1394
10.1088/0026-1394/44/5/014
ILira
CElster
WWöger
article
Elster2007
Draft GUM Supplement 1 and Bayesian analysis
Metrologia
2007
44
3
L31--L32
8.42,Bayes,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/44/3/N03
IOP Publishing
en
0026-1394
10.1088/0026-1394/44/3/N03
CElster
WWöger
M GCox
article
Elster2007b
Analysis of dynamic measurements and determination of time-dependent measurement uncertainty using a second-order model
Measurement Science and Technology
2007
18
12
3682-3687
dynamic measurement
8.42,Dynamik, Unsicherheit
IOP Publishing
en
10.1088/0957-0233/18/12/002
CElster
ALink
TBruns
article
Elster2007a
Calculation of uncertainty in the presence of prior knowledge
Metrologia
2007
44
2
111--116
8.42,Unsicherheit
http://iopscience.iop.org/article/10.1088/0026-1394/44/2/002
IOP Publishing
en
0026-1394
10.1088/0026-1394/44/2/002
CElster
article
Elster2005a
Quantitative magnetic resonance spectroscopy: semi-parametric modeling and determination of uncertainties
Magnetic resonance in medicine
2005
53
6
1288--96
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<prt>é</prt>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<prt>é</prt>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<prt>é</prt>mer-Rao lower bounds, which tend to overestimate accuracy.
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
8.42, Unsicherheit, in-vivo
http://www.ncbi.nlm.nih.gov/pubmed/15906296
0740-3194
10.1002/mrm.20500
CElster
FSchubert
ALink
MWalzel
FSeifert
HRinneberg