Abstract Model calibration is crucial for optimizing the performance of complex computer
models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological …

Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count
data because, in contrast to real-valued alternatives such as factor analysis, it produces an …

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 …

Occupancy modelling is a common approach to assess species distribution patterns, while
explicitly accounting for false absences in detection–nondetection data. Numerous …

Gaussian processes (GPs) are commonly used as models for functions, time series, and
spatial fields, but they are computationally infeasible for large datasets. Focusing on the …

Feature selection to identify spatially variable genes or other biologically informative genes
is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose …

Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process
(GP) distributed spatial random effect, are widely used for analyses of geospatial data. We …

Variational approximations to Gaussian processes (GPs) typically use a small set of
inducing points to form a low-rank approximation to the covariance matrix. In this work, we …

We introduce a novel way to combine boosting with Gaussian process and mixed effects
models. This allows for relaxing, first, the zero or linearity assumption for the prior mean …

Satellite remote sensing has now become a unique tool for continuous and predictable
monitoring of geosystems at various scales, observing the dynamics of different geophysical …