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arXiv · 1912.08843

Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures

Taehee Lee·Charles E. Lawrence
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Abstract

To restore the historical sea surface temperatures (SSTs) better, it is important to construct a good calibration model for the associated proxies. In this paper, we introduce a new model for alkenone (${\rm{U}}_{37}^{\rm{K}'}$) based on the heteroscedastic Gaussian process (GP) regression method. Our nonparametric approach not only deals with the variable pattern of noises over SSTs but also contains a Bayesian method of classifying potential outliers.

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Taehee Lee, Charles E. Lawrence. 2019-12-18. Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures. https://doi.org/10.5065/y82j-f154

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