The role of the nugget term in the Gaussian process method
Andrey Pepelyshev
TL;DR
This paper investigates how including a nugget term affects the maximum likelihood estimation of the correlation parameter in Gaussian processes, especially for deterministic models.
Contribution
It provides a comparative analysis of correlation parameter estimates with and without the nugget term in Gaussian process models for deterministic data.
Findings
Nugget term influences the maximum likelihood estimate of the correlation parameter.
Including a nugget can improve model fit for deterministic models.
The study offers insights into the estimation process with and without the nugget term.
Abstract
The maximum likelihood estimate of the correlation parameter of a Gaussian process with and without of a nugget term is studied in the case of the analysis of deterministic models.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnalysis of environmental and stochastic processes · Atmospheric and Environmental Gas Dynamics · Statistical and Computational Modeling