The use of complex statistical models has recently increased substantially in the context of
species distribution behavior. This complexity has made the inferential and predictive …

Spatial interpolation has been frequently encountered in earth sciences and engineering. A
reasonable appraisal of subsurface heterogeneity plays a significant role in planning, risk …

Gaussian processes are valuable tools for non-parametric modelling, where typically an
assumption of stationarity is employed. While removing this assumption can improve …

Gridded data products, for example interpolated daily measurements of precipitation from
weather stations, are commonly used as a convenient substitute for direct observations …

Kriging is the predominant method used for spatial prediction, but relies on the assumption
that predictions are linear combinations of the observations. Kriging often also relies on …

While various studies explore the relationship between individual sources of climate
variability and extreme precipitation, there is a need for improved understanding of how …

Many methods for modeling functions over high-dimensional spaces assume global
smoothness properties; such assumptions are often violated in practice. We introduce a …

A multivariate distribution can be described by a triangular transport map from the target
distribution to a simple reference distribution. We propose Bayesian nonparametric …

Predicting the response at an unobserved location is a fundamental problem in spatial
statistics. Given the difficulty in modeling spatial dependence, especially in nonstationary …

Modeling data with nonstationary covariance structure is important to represent
heterogeneity in geophysical and other environmental spatial processes. In this work, we …