In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …

Artificial intelligence for social good (AI4SG) is a research theme that aims to use and
advance artificial intelligence to address societal issues and improve the well-being of the …

Abstract Bayesian Optimization using Gaussian Processes is a popular approach to deal
with optimization involving expensive black-box functions. However, because of the …

Robotic sampling is attractive in many field robotics applications that require persistent
collection of physical samples for ex-situ analysis. Examples abound in the earth sciences in …

Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued
Data (with Discussion) Page 1 Bayesian Analysis (2018) 13, Number 1, pp. 253–310 …

Understanding and predicting how influenza propagates is vital to reduce its impact. In this
paper we develop a nonparametric model based on Gaussian process (GP) regression to …

Bayesian Optimization using Gaussian Processes is a popular approach to deal with the
optimization of expensive black-box functions. However, because of the a priori on the …

We propose to model a spatio‐temporal random field that has nonstationary covariance
structure in both space and time domains by applying the concept of the dimension …

Rotating machinery is essential to modern life, from power generation to transportation and
a host of other industrial applications. Since such equipment generally operates under …

Mobile robots are increasingly used to collect valuable in situ samples during scientific
expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal …