152 citations · 762 across the 23 of their papers we have counts for
28 papers
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…
Vecchia Likelihood Approximation for Accurate and Fast Inference in Intractable Spatial Extremes Models
Raphaël Huser, Michael L. Stein, Peng Zhong
Max-stable processes are the most popular models for high-impact spatial extreme events, as they arise as the only possible limits of spatially-indexed block maxima. However, likel…
Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails
Mitchell Krock, Julie Bessac, Michael L. Stein +1
In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values…
Neural Networks for Parameter Estimation in Intractable Models
Amanda Lenzi, Julie Bessac, Johann Rudi +1
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate par…
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…