Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty.

Uncertainty quantification (UQ) is increasingly critical for modelling complex systems in which input parameters or environmental conditions vary unpredictably. Polynomial chaos methods offer a ...

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 ...

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 ...

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 ...

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 ...

Abstract: The increasing power of computing platforms and the recent advances in data science techniques have fostered the development of data-driven computational models of engineering systems with ...

(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.