189 citations · 522 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
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…
Inverses of Matern Covariances on Grids
Joseph Guinness
We conduct a study of the aliased spectral densities of Matérn covariance functions on a regular grid of points, providing clarity on the properties of a popular approximation base…
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation
Joseph Guinness
We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally effi…
Non-Stationary Covariance Estimation using the Stochastic Score Approximation for Large Spatial Data
Amanda Muyskens, Joseph Guinness, Montserrat Fuentes
We introduce computational methods that allow for effective estimation of a flexible, parametric non-stationary spatial model when the field size is too large to compute the multiv…
Permutation and Grouping Methods for Sharpening Gaussian Process Approximations
Joseph Guinness
Vecchia's approximate likelihood for Gaussian process parameters depends on how the observations are ordered, which can be viewed as a deficiency because the exact likelihood is pe…
Efficient Computation of Gaussian Likelihoods for Stationary Markov Random Field Models
Joseph Guinness, Ilse C. F. Ipsen
Rue and Held (2005) proposed a method for efficiently computing the Gaussian likelihood for stationary Markov random field models, when the data locations fall on a complete regula…