12 papers
Bigraphical Matérn-Whittle (BMW) Processes for Fast Inference of Big Multivariate Spatial Data on General Domains
Debangan Dey, Alokesh Manna, Christopher J. Geoga
Large spatial data sets now record many correlated variables at many thousands of locations, often on domains where Euclidean distance misrepresents proximity. The central difficul…
Linear-cost Polyharmonic Spline Interpolation of Arbitrary Degree
Christopher J. Geoga, Michael O'Neil
We introduce a simple and performant approach for rapidly and accurately performing polyharmonic spline (PHS) interpolation using a combination of the fast multipole method (FMM) f…
A Gaussian Process Model of 3D Udder Point Clouds for Teat Length Phenotyping in Dairy Cows
Maria E. Montes, João R. R. Doréa, Christopher J. Geoga
Phenotyping conformation traits is important for dairy cattle breeding and management. Although large-scale phenotyping is possible with 3D imaging technologies, manual annotation…
Fast and accurate conditioning for large-scale Gaussian process prediction problems
Samanyu Arora, Christopher J. Geoga
Gaussian Process (GP) models provide a flexible framework for prediction and uncertainty quantification. For most covariance functions, however, exact GP prediction with points…
High-Resolution Retrieval of Atmospheric Boundary Layers with Nonstationary Gaussian Processes
Haoran Xiong, Paytsar Muradyan, Christopher J. Geoga
The atmospheric boundary layer (ABL) plays a critical role in governing turbulent exchanges of momentum, heat moisture, and trace gases between the Earth's surface and the free atm…
Fast nonparametric spectral density estimation from irregularly sampled data
Christopher J. Geoga, Paul G. Beckman
We introduce a nonparametric spectral density estimator for continuous-time and continuous-space processes measured at fully irregular locations. Our estimator is constructed using…
Fast Adaptive Fourier Integration for Spectral Densities of Gaussian Processes
Paul G. Beckman, Christopher J. Geoga
The specification of a covariance function is of paramount importance when employing Gaussian process models, but the requirement of positive definiteness severely limits those use…
Fast Machine-Precision Spectral Likelihoods for Stationary Time Series
Christopher J. Geoga
We provide in this work an algorithm for approximating a very broad class of symmetric Toeplitz matrices to machine precision in time with applications to f…
A Scalable Method to Exploit Screening in Gaussian Process Models with Noise
Christopher J. Geoga, Michael L. Stein
A common approach to approximating Gaussian log-likelihoods at scale exploits the fact that precision matrices can be well-approximated by sparse matrices in some circumstances. Th…
Scalable Computations for Nonstationary Gaussian Processes
Paul G. Beckman, Christopher J. Geoga, Michael L. Stein +1
Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial datasets. However, the large number of observations in modern datasets…
Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data
Christopher J. Geoga, Mihai Anitescu, Michael L. Stein
Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of ha…
Scalable Gaussian Process Computations Using Hierarchical Matrices
Christopher J. Geoga, Mihai Anitescu, Michael L. Stein
We present a kernel-independent method that applies hierarchical matrices to the problem of maximum likelihood estimation for Gaussian processes. The proposed approximation provide…