Uncertainty quantification (UQ) is increasingly critical for modelling complex systems in which input parameters or environmental conditions vary unpredictably. Polynomial chaos methods offer a ...
ABSTRACT A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability ...
The newest journal from the Society for Industrial and Applied Mathematics, SIAM/ASA Journal on Uncertainty Quantification (JUQ), launched today with its first seven papers publishing online to Volume ...
A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...
When we use simulation to estimate the performance of a stochastic system, the simulation often contains input models that were estimated from real-world data; therefore, there is both simulation and ...
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial ...
(Nanowerk Spotlight) Standards, in a scientific context, have a much larger role in our society than just agreeing measurements. As the British Standards Institution (BSI) explains it, put at its ...
Quantifying uncertainty in carbon accounting is essential at scales ranging from individual projects to country-level compensation for reducing emissions from deforestation and forest degradation.
When it comes to making economic decisions around reservoir management, whether it be bid valuations, new field development and operational plans, production estimates, or divestments, there is one ...
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