The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …

Deep learning applies hierarchical layers of hidden variables to construct nonlinear high
dimensional predictors. Our goal is to develop and train deep learning architectures for …

Integro-difference equation (IDE) models describe the conditional dependence between the
spatial process at a future time point and the process at the present time point through an …

We describe the details related to the proposed approach HDCM, including the datasets, the
competitive models, the selection of the tuning parameters, the simulation study for the …

Integro-difference equations (IDEs) provide a flexible framework for dynamic modeling of
spatio-temporal data. The choice of kernel in an IDE model relates directly to the underlying …

An integro-difference equation can be represented as a hierarchical spatiotemporal dynamic
model using appropriate parameterizations. The dynamics of the process defined by an …

Discussion of paper “nonparametric Bayesian inference in applications” by Peter Müller,
Fernando A. Quintana and Garritt L. Page | SpringerLink Skip to main content Advertisement …

To this day, terrorism perpetrated by non-state actors persists as a worldwide threat, as
exemplified by the recent lethal attacks in Paris, London, Brussels, and the ongoing …

Remote sensing of the earth by satellites yields datasets that can be massive in size. To
overcome computational challenges, we make use of the reduced-rank Spatial Random …