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A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data.
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications.
Review: Nonstationary Spatial Modeling, with Emphasis on Process Convolution and Covariate-Driven Approaches
In many environmental applications involving spatially-referenced data, limitations on the number and locations of observations motivate the need for practical and efficient models for spatial interpolation, or kriging.
Methodology
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