Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties,
and the environment in many areas. Investigators have for long attempted to estimate …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

Landslide susceptibility corresponds to the probability of landslide occurrence across a
given geographic space. This probability is usually estimated by using a binary classifier …

The classical modeling of spatial extremes relies on asymptotic models (ie, max‐stable or r‐
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …

The supplementary material contains the following: a PDF document containing plots for the
inspection of posterior predictive densities, plots showing regionalized predictions, kernel …

The health consequences of climate variability and change are diverse, potentially affecting
the burden of a wide range of health outcomes, including illnesses and deaths related to …

We investigate earthquake‐induced landslides using a geostatistical model featuring a
latent spatial effect (LSE). The LSE represents the spatially structured residuals in the data …

The generalized extreme value (GEV) distribution is the only possible limiting distribution of
properly normalized maxima of a sequence of independent and identically distributed …

Analyzing Stochastic Computer Models: A Review with Opportunities Page 1 Statistical
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …