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 …

We described the key features of the pragmatic geostatistical methodology aiming at
resolving the following drawbacks of classical geostatistical models: assuming that the data …

A fundamental decision to make during the analysis of geostatistical data is the modeling of
the spatial dependence structure as stationary or non-stationary. Although second-order …

In this paper, we introduce a new spatial model that incorporates heteroscedastic variance
depending on neighbouring locations. The proposed process is considered as the spatial …

An income distribution describes how an entity's total wealth is distributed amongst its
population. A problem of interest to regional economics researchers is to understand the …

There has been a long-standing challenge in develo** locally stationary Gaussian
process models concerning how to obtain flexible partitions and make predictions near …

This paper presents a Gaussian process (GP) model for estimating piecewise continuous
regression functions. In many scientific and engineering applications of regression analysis …

Distributed Bayesian Inference in Massive Spatial Data Page 1 Statistical Science 2023, Vol.
38, No. 2, 262–284 https://doi.org/10.1214/22-STS868 © Institute of Mathematical Statistics …

Active learning is a subfield of machine learning that focuses on improving the data
collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate …

Data from remote sensing with finer spectral and spatial resolution are increasingly
available. While this allows more accurate prediction of plant traits at different spatial scales …