On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Background Estimating the health effects of multi-pollutant mixtures is of increasing interest
in environmental epidemiology. Recently, a new approach for estimating the health effects of …

Geostatistical data—spatially referenced observations related to some continuous spatial
phenomenon—are ubiquitous in ecology and can reveal ecological processes and inform …

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …

Purpose There are growing signs that the COVID‐19 virus has started to spread to rural
areas and can impact the rural health care system that is already stretched and lacks …

Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support
interdisplinary fisheries management in the US and worldwide. These assessment activities …

The principles behind the interface to continuous domain spatial models in the RINLA
software package for R are described. The integrated nested Laplace approximation (INLA) …

Spatial process models for analyzing geostatistical data entail computations that become
prohibitive as the number of spatial locations become large. This article develops a class of …