Dense kernel matrices resulting from pairwise evaluations of a kernel
fu...
For regression tasks, standard Gaussian processes (GPs) provide natural
...
To achieve scalable and accurate inference for latent Gaussian processes...
Rapid developments in satellite remote-sensing technology have enabled t...
We propose an approximation to the forward-filter-backward-sampler (FFBS...
Bayesian optimization is a technique for optimizing black-box target
fun...
Gaussian process (GP) regression is a flexible, nonparametric approach t...
Gaussian processes are widely used as priors for unknown functions in
st...
Earth-observing satellite instruments obtain a massive number of observa...
Potts models, which can be used to analyze dependent observations on a
l...
A multivariate distribution can be described by a triangular transport m...
In spatial statistics, it is often assumed that the spatial field of int...
Spatial statistics often involves Cholesky decomposition of covariance
m...
Nitrogen dioxide (NO_2) is a primary constituent of traffic-related air
...
Many scientific phenomena are studied using computer experiments consist...
We propose to compute a sparse approximate inverse Cholesky factor L of ...
Generalized Gaussian processes (GGPs) are highly flexible models that co...
Spatio-temporal data sets are rapidly growing in size. For example,
envi...
People are increasingly concerned with understanding their personal
envi...
Gaussian processes (GPs) are highly flexible function estimators used fo...
Gaussian processes are popular and flexible models for spatial, temporal...
Gaussian processes (GPs) are commonly used as models for functions, time...