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

Abstract Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …

Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented
computing roadblocks in the analysis of large geospatial datasets using GPs have now …

Many scientific phenomena are studied using computer experiments consisting of multiple
runs of a computer model while varying the input settings. Gaussian processes (GPs) are a …

We derive a single-pass algorithm for computing the gradient and Fisher information of
Vecchia's Gaussian process loglikelihood approximation, which provides a computationally …

Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal
modelling and prediction. These techniques typically presuppose that the data are observed …

Adjusting for an unmeasured confounder is generally an intractable problem, but in the
spatial setting it may be possible under certain conditions. We derive necessary conditions …

As spatial datasets are becoming increasingly large and unwieldy, exact inference on
spatial models becomes computationally prohibitive. Various approximation methods have …

Given the increasing prevalence of wildland fires in the Western US, there is a critical need
to develop tools to understand and accurately predict burn severity. We develop a novel …

Given the computational challenges involved in calculating the maximum likelihood
estimates for large spatial datasets, there has been significant interest in the research …