Results 31 to 40 of about 26,410,412 (273)
Uncertainty Analysis for Data-Driven Chance-Constrained Optimization [PDF]
, 2020 In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations.
Esche, Erik +3 more
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Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6 [PDF]
, 2012 A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in ...
Briggs, A.H. +5 more
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Assessment of flood damages and benefits of remedial actions: "What are the weak links?"; with application to the Loire [PDF]
, 1995 Flood damage models are used to determine the impact of measures to reduce damage due to river flooding. Such models are characterized by uncertainty. This uncertainty may affect the decisions made on the basis of the model outcomes.
Blois, C.J. de, Wind, H.G.
core +2 more sources
Towards Best Practice Framing of Uncertainty in Scientific Publications: A Review of Water Resources Research Abstracts [PDF]
, 2017 Uncertainty is recognized as a key issue in water resources research, amongst other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g. uncertainty quantification and model validation.
Elsawah, Sondoss +4 more
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Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model
Yuanzineng kexue jishu, 2023 In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
doaj
COVID-19 Genome Sequence Analysis for New Variant Prediction and Generation
Mathematics, 2022 The new COVID-19 variants of concern are causing more infections and spreading much faster than their predecessors. Recent cases show that even vaccinated people are highly affected by these new variants.
Amin Ullah +7 more
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ProbCD: enrichment analysis accounting for categorization uncertainty [PDF]
, 2007 As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or ...
A Lewin +22 more
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Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems
Applied Sciences, 2022 This paper consists of evaluating the performance of a vibro-acoustic model in the presence of uncertainties in the geometric and material parameters of the model using Monte Carlo simulations (MCS).
Ahmad Baklouti +2 more
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The Effect Size in Uncertainty Analysis [PDF]
Value in Health, 2010 In model-based health economic evaluation, uncertainty analysis is often done using parametric bootstrapping. This requires specifying probability distributions for the model variables that are uncertain.The effect size of the intervention is often expressed as a relative risk, and the standard assumption for a relative risk is that it has a lognormal ...
openaire +3 more sources
Hazard Characterization of Modified Vaccinia Virus Ankara Vector: What Are the Knowledge Gaps?
Viruses, 2017 Modified vaccinia virus Ankara (MVA) is the vector of choice for human and veterinary applications due to its strong safety profile and immunogenicity in vivo.
Malachy I. Okeke +7 more
doaj +1 more source