189 citations · 763 across the 33 of their papers we have counts for
19 papers · 1 filter
Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
Carrie J. Lei-Cramer, Jian Cao, Matthias Katzfuss
Generative modeling of spatio-temporal fields is crucial for a variety of applications, including stochastic weather generators and climate-model surrogates. However, many such fie…
Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields
Paul F. V. Wiemann, Matthias Katzfuss
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the…
Locally anisotropic covariance functions on the sphere
Jian Cao, Jingjie Zhang, Zhuoer Sun +1
Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that…
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition
Marcin Jurek, Matthias Katzfuss
We propose an approximation to the forward-filter-backward-sampler (FFBS) algorithm for large-scale spatio-temporal smoothing. FFBS is commonly used in Bayesian statistics when wor…
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton +1
Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covari…
Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields
Matthias Katzfuss, Florian Schäfer
A multivariate distribution can be described by a triangular transport map from the target distribution to a simple reference distribution. We propose Bayesian nonparametric infere…
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…
Scaled Vecchia approximation for fast computer-model emulation
Matthias Katzfuss, Joseph Guinness, Earl Lawrence
Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a po…
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…
Vecchia approximations of Gaussian-process predictions
Matthias Katzfuss, Joseph Guinness, Wenlong Gong +1
Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasib…
A class of multi-resolution approximations for large spatial datasets
Matthias Katzfuss, Wenlong Gong
Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-proce…
A Case Study Competition Among Methods for Analyzing Large Spatial Data
Matthew J. Heaton, Abhirup Datta, Andrew Finley +10
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationall…
A general framework for Vecchia approximations of Gaussian processes
Matthias Katzfuss, Joseph Guinness
Gaussian processes (GPs) are commonly used as models for functions, time series, and spatial fields, but they are computationally infeasible for large datasets. Focusing on the typ…
Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models
Matthias Katzfuss, Jonathan R. Stroud, Christopher K. Wikle
We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combin…
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
Jonathan R. Stroud, Matthias Katzfuss, Christopher K. Wikle
This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior…
A multi-resolution approximation for massive spatial datasets
Matthias Katzfuss
Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of high-resolution observations of spatial fields over large spatial regions…
Interpretation of point forecasts with unkown directive
Patrick Schmidt, Matthias Katzfuß, Tilmann Gneiting
Point forecasts can be interpreted as functionals (i.e., point summaries) of predictive distributions. We consider the situation where forecasters' directives are hidden and develo…
Bayesian Nonstationary Spatial Modeling for Very Large Datasets
Matthias Katzfuss
With the proliferation of modern high-resolution measuring instruments mounted on satellites, planes, ground-based vehicles and monitoring stations, a need has arisen for statistic…