2 citations · 3 across the 4 of their papers we have counts for
9 papers
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Felix Jimenez, Matthias Katzfuss
Bayesian optimization is a technique for optimizing black-box target functions. At the core of Bayesian optimization is a surrogate model that predicts the output of the target fun…
Ordered conditional approximation of Potts models
Anirban Chakraborty, Matthias Katzfuss, Joseph Guinness
Potts models, which can be used to analyze dependent observations on a lattice, have seen widespread application in a variety of areas, including statistical mechanics, neuroscienc…
Bayesian nonstationary and nonparametric covariance estimation for large spatial data
Brian Kidd, Matthias Katzfuss
In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropria…
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide
Kyle P Messier, Matthias Katzfuss
Nitrogen dioxide (NO) is a primary constituent of traffic-related air pollution and has well established harmful environmental and human-health impacts. Knowledge of the spatio…
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
Daniel Zilber, Matthias Katzfuss
Generalized Gaussian processes (GGPs) are highly flexible models that combine latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can be used in…
Multi-resolution filters for massive spatio-temporal data
Marcin Jurek, Matthias Katzfuss
Spatio-temporal data sets are rapidly growing in size. For example, environmental variables are measured with ever-higher resolution by increasing numbers of automated sensors moun…