GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.
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Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.
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Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.
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Choosing the threshold in extreme value analysis
Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.