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Published in 2024 at "Statistics in Medicine"
DOI: 10.1002/sim.10309
Abstract: A common problem in numerous research areas, particularly in clinical trials, is to test whether the effect of an explanatory variable on an outcome variable is equivalent across different groups. In practice, these tests are…
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Keywords:
model averaging;
equivalence;
overcoming model;
model ... See more keywords
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Published in 2021 at "Journal of High Energy Physics"
DOI: 10.1007/jhep10(2021)110
Abstract: Abstract The pursuit of experimental precision in the CP-violating weak phase ϕ2 (α) is not without its challenges, in part due to the need to combine multiple physical observables from various related decay channels, and…
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Keywords:
case;
systematic amplitude;
model;
model uncertainty ... See more keywords
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Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06528-z
Abstract: Artificial intelligence systems are becoming ubiquitous in everyday life as well as in high-risk environments, such as autonomous driving, medical treatment, and medicine. The opaque nature of the deep neural network raises concerns about its…
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Keywords:
counterfactual explanation;
model;
bayesian model;
model uncertainty ... See more keywords
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Published in 2021 at "Engineering Structures"
DOI: 10.1016/j.engstruct.2021.112290
Abstract: Abstract This paper concerns the design of lap splice lengths for ribbed steel reinforcement bars embedded in concrete produced with coarse recycled concrete aggregates. Recycled aggregates are weaker and typically lead to concrete with lower…
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Keywords:
aggregate concrete;
model;
model uncertainty;
bond ... See more keywords
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Published in 2018 at "Ocean Engineering"
DOI: 10.1016/j.oceaneng.2017.11.001
Abstract: Abstract This work quantifies the model uncertainty in the wave-induced motion calculations of the codes in the Offshore Code Comparison Study Phase II, Semisubmersible Platform Based Floating Wind Turbine. Root mean square values are used…
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Keywords:
uncertainty;
uncertainty wave;
wave induced;
wind turbine ... See more keywords
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Published in 2019 at "Progress in Aerospace Sciences"
DOI: 10.1016/j.paerosci.2018.10.001
Abstract: In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important role in decades to come. However, model uncertainties are still a major obstacle…
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Keywords:
simulations review;
quantification model;
uncertainty;
model uncertainty ... See more keywords
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Published in 2019 at "Probabilistic Engineering Mechanics"
DOI: 10.1016/j.probengmech.2018.08.004
Abstract: Abstract We discuss Bayesian inference for the identification of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in the strain measurements. Since a difference…
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Keywords:
input;
model;
model uncertainty;
error model ... See more keywords
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Published in 2024 at "Scandinavian Actuarial Journal"
DOI: 10.1080/03461238.2024.2421852
Abstract: In actuarial modeling, right-skewed distributions are of paramount importance. Typically, they have the property of becoming unimodal and symmetric after applying some increasing concave transformation $ \mathsf {T} $ T (e.g. log-transformation). In what follows,…
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Keywords:
symmetric right;
uncertainty assessment;
assessment symmetric;
right skewed ... See more keywords
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Published in 2021 at "Journal of Physics D: Applied Physics"
DOI: 10.1088/1361-6463/ac2ad4
Abstract: The solution of an inverse problem in computational imaging (CI) often requires the knowledge of the physical model and/or the object. However, in many practical applications, the physical model may not be accurately characterized, leading…
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Keywords:
learning approach;
model;
untrained learning;
computational imaging ... See more keywords
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Published in 2024 at "Royal Society Open Science"
DOI: 10.1098/rsos.230803
Abstract: The COVID-19 pandemic illustrated many of the challenges with using science to guide planning and policymaking. One such challenge has to do with how to manage, represent and communicate uncertainties in epidemiological models. This is…
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Keywords:
covid pandemic;
science;
policy;
science policy ... See more keywords
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Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2876883
Abstract: Kalman filters (KFs) and dynamic observers are two main classes of the dynamic state estimation (DSE) routines. The Power system DSE has been implemented by various KFs, such as the extended KF (EKF) and the…
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Keywords:
power system;
cyber attacks;
model uncertainty;