تقرير
Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures
| العنوان: | Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures |
|---|---|
| المؤلفون: | Lee, Taehee, Lawrence, Charles E. |
| سنة النشر: | 2019 |
| المجموعة: | Statistics |
| مصطلحات موضوعية: | Statistics - Applications, Statistics - Machine Learning |
| الوصف: | 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. Comment: This article has been submitted to "Dec 2019, Proceedings of the 9th International Workshop on Climate Informatics: CI 2019. NCAR Technical Note NCAR/TN-561+PROC" |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.5065/y82j-f154 |
| URL الوصول: | http://arxiv.org/abs/1912.08843 |
| رقم الأكسشن: | edsarx.1912.08843 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.5065/y82j-f154 |
|---|