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09/18/2023
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
The Gaussian process (GP) is a popular statistical technique for stochas...
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07/25/2023
A flexible class of priors for conducting posterior inference on structured orthonormal matrices
The big data era of science and technology motivates statistical modelin...
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07/07/2023
Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave
In late June, 2021, a devastating heatwave affected the US Pacific North...
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05/18/2022
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
A Gaussian Process (GP) is a prominent mathematical framework for stocha...
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11/12/2019
The effect of geographic sampling on extreme precipitation: from models to observations and back again
In light of the significant uncertainties present in global climate mode...
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10/30/2019
Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R
In spite of the diverse literature on nonstationary Gaussian process mod...
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02/15/2019
Detected changes in precipitation extremes at their native scales derived from in situ measurements
The gridding of daily accumulated precipitation--especially extremes--fr...
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07/11/2018