Computer models play a key role in many scientific and engineering problems. One major
source of uncertainty in computer model experiments is input parameter uncertainty …

In modeling spatial processes, a second-order stationarity assumption is often made.
However, for spatial data observed on a vast domain, the covariance function often varies …

Abstract The International Commission on Illumination (CIE) designed its color space to be
perceptually uniform so that a given numerical change in the color code corresponds to …

Bayesian methods for modelling and inference are being increasingly used in the
cryospheric sciences and glaciology in particular. Here, we present a review of recent works …

Computer models play a key role in many scientific and engineering problems. One major
source of uncertainty in computer model experiment is input parameter uncertainty …

Abstract Fine particulate matter, PM _ 2.5 2.5, has been documented to have adverse health
effects, and wildland fires are a major contributor to PM _ 2.5 PM 2.5 air pollution in the USA …

Uncertainty Quantification (UQ) is an umbrella term referring to a broad class of methods
which typically involve the combination of computational modeling, experimental data and …

El análisis de calibración de modelos de aprendizaje de máquina cobra gran importancia
en distintos contextos como evaluación del riesgo, diagnósticos y sistemas críticos para la …

The modeling of spatio-temporal and multivariate spatial random fields has been an
important and growing area of research due to the increasing availability of spacetime …

Advancements in storage capabilities have resulted in a massive influx of data that can be
used to improve many aspects of the human society. Sometimes these data sets can be …