189 citations · 763 across the 33 of their papers we have counts for
6 papers · 1 filter
Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
Anirban Chakraborty, Matthias Katzfuss
Many applications, including climate-model analysis and stochastic weather generators, require learning or emulating the distribution of a high-dimensional and non-Gaussian spatial…
Sparse inverse Cholesky factorization of dense kernel matrices by greedy conditional selection
Stephen Huan, Joseph Guinness, Matthias Katzfuss +2
Dense kernel matrices resulting from pairwise evaluations of a kernel function arise naturally in machine learning and statistics. Previous work in constructing sparse approximate…
Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference
Myeongjong Kang, Matthias Katzfuss
Gaussian processes are widely used as priors for unknown functions in statistics and machine learning. To achieve computationally feasible inference for large datasets, a popular a…
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…
Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering
Marcin Jurek, Matthias Katzfuss
Spatial statistics often involves Cholesky decomposition of covariance matrices. To ensure scalability to high dimensions, several recent approximations have assumed a sparse Chole…
Parallel inference for massive distributed spatial data using low-rank models
Matthias Katzfuss, Dorit Hammerling
Due to rapid data growth, statistical analysis of massive datasets often has to be carried out in a distributed fashion, either because several datasets stored in separate physical…