Results 31 to 40 of about 401,511 (278)

Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors [PDF]

, 2009
Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural ...
Atger, Benjamin Renard, Beven, Beven, Beven, Bras, Bulygina, Di Baldassarre, Dmitri Kavetski, Dottori, Duan, Eberly, Fenicia, Fenicia, Feyen, Franks, Gelfand, Gelman, George Kuczera, Goldstein, Haario, Hadamard, Hall, Huard, Jacquin, Kavetski, Kavetski, Kavetski, Kavetski, Kennedy, Krzysztofowicz, Kuczera, Kuczera, Kuczera, Laio, Mantovan, Mark Thyer, Marshall, Montanari, Montanari, Moradkhani, Moyeed, Neppel, Oudin, Perrin, Refsgaard, Reichert, Reitan, Renard, Severino, Spiegelhalter, Stedinger, Stewart W. Franks, Tarantola, Thiemann, Thyer, Tonkin, Wagener, Yang, Young   +59 more
core   +3 more sources

UQpy v4.1: Uncertainty quantification with Python

SoftwareX, 2023
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to ...
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukas Novak, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner   +11 more
doaj   +1 more source

Frequentist coverage of adaptive nonparametric Bayesian credible sets [PDF]

, 2015
We investigate the frequentist coverage of Bayesian credible sets in a nonparametric setting. We consider a scale of priors of varying regularity and choose the regularity by an empirical Bayes method.
Szabó, Botond, van der Vaart, A. W., van Zanten, J. H.   +2 more
core   +4 more sources

Optimal Power Procurement for Green Cellular Wireless Networks Under Uncertainty and Chance Constraints

Entropy
Given the increasing global emphasis on sustainable energy usage and the rising energy demands of cellular wireless networks, this work seeks an optimal short-term, continuous-time power-procurement schedule to minimize operating expenditure and the ...
Nadhir Ben Rached, Shyam Mohan Subbiah Pillai, Raúl Tempone   +2 more
doaj   +1 more source

Complex model calibration through emulation, a worked example for a stochastic epidemic model

Epidemics, 2022
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology,
Michael Dunne, Hossein Mohammadi, Peter Challenor, Rita Borgo, Thibaud Porphyre, Ian Vernon, Elif E. Firat, Cagatay Turkay, Thomas Torsney-Weir, Michael Goldstein, Richard Reeve, Hui Fang, Ben Swallow   +12 more
doaj   +1 more source

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change [PDF]

Earth System Dynamics
This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario ...
J. D. Jakeman, M. Perego, D. T. Seidl, T. A. Hartland, T. R. Hillebrand, M. J. Hoffman, S. F. Price   +6 more
doaj   +1 more source

Principal component density estimation for scenario generation using normalizing flows

Data-Centric Engineering, 2022
Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources, such as photovoltaics (PV) and wind as well as load demands, has recently gained attention.
Eike Cramer, Alexander Mitsos, Raúl Tempone, Manuel Dahmen   +3 more
doaj   +1 more source

Democratizing uncertainty quantification

Journal of Computational Physics
Add Benjamin Kent as co-author in accordance with the paper's published ...
Seelinger L., Reinarz A., Lykkegaard M. B., Akers R., Alghamdi A. M. A., Aristoff D., Bangerth W., Bénézech J., Diez M., Frey K., Jakeman J. D., Jørgensen J. S., Kim K. T., Kent B. M., Martinelli M., Parno M., Pellegrini R., Petra N., Riis N. A. B., Rosenfeld K., Serani A., Tamellini L., Villa U., Dodwell T. J., Scheichl R.   +24 more
openaire   +6 more sources

Uncertainty quantification in breakup reactions

Physical Review C, 2022
Breakup reactions are one of the favored probes to study loosely bound nuclei, particularly those in the limit of stability forming a halo. In order to interpret such breakup experiments, the continuum discretized coupled channel method is typically used. In this study, the first Bayesian analysis of a breakup reaction model is performed.
Ö. Sürer, F. M. Nunes, M. Plumlee, S. M. Wild   +3 more
openaire   +4 more sources

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Entropy, 2021
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings.
Benjamin Kompa, Jasper Snoek, Andrew L. Beam   +2 more
doaj   +1 more source