Gaussian processes and random fields have a long history, covering multiple approaches to
representing spatial and spatio-temporal dependence structures, such as covariance …

We define a new class of Gaussian processes on compact metric graphs such as street or
river networks. The proposed models, the Whittle–Matérn fields, are defined via a fractional …

Gaussian processes (GPs) are used to make medical and scientific decisions, including in
cardiac care and monitoring of atmospheric carbon dioxide levels. Notably, the choice of GP …

Equivalence of measures and asymptotically optimal linear prediction for Gaussian random
fields with fractional-order covariance Page 1 Bernoulli 29(2), 2023, 1476–1504 https://doi.org/10.3150/22-BEJ1507 …

Whittle-Mat\'ern fields are a recently introduced class of Gaussian processes on metric
graphs, which are specified as solutions to a fractional-order stochastic differential equation …

Gaussian processes are widely employed as versatile modelling and predictive tools in
spatial statistics, functional data analysis, computer modelling and diverse applications of …

The distribution of centered Gaussian random fields (GRFs) indexed by compacta such as
smooth, bounded Euclidean domains or smooth, compact and orientable manifolds is …

We consider Gaussian measures µ, µ on a separable Hilbert space, with fractional-order
covariance operators A− 2β resp. A− 2 β, and derive necessary and sufficient conditions on …

We define various higher-order Markov properties for stochastic processes $(X (t)) _
{t\in\mathbb {T}} $, indexed by an interval $\mathbb {T}\subseteq\mathbb {R} $ and taking …

The estimation of regression parameters in spatially referenced data plays a crucial role
across various scientific domains. A common approach involves employing an additive …