Work

Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures

Year: 2019

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1912.08843

Authors: Lee, Taehee, Lawrence, Charles E.

Institution: Brown University

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Brown University, Kwanjeong Educational Foundation

Award: Collaborative Research: Bringing the Late Pleistocene into Focus: Better estimates of Ages and Ocean Circulation through Data-Model Comparison

OpenAlex ID: W2995905558

Cites: 16

Cited by: 14

Related to: 10