Selection of Robust Features for Gaussian Process Regression to Predict COVID-19 Case Rates

Singh, Yuvraj (2025) Selection of Robust Features for Gaussian Process Regression to Predict COVID-19 Case Rates. Master's, Georg-August-Universität Göttingen.

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Item URL in elib:

https://elib.dlr.de/219883/

Document Type:

Thesis (Master's)

Title:

Selection of Robust Features for Gaussian Process Regression to Predict COVID-19 Case Rates

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Singh, Yuvraj	yuvraj8912 (at) gmail.com	UNSPECIFIED	UNSPECIFIED

DLR Supervisors:

Contribution	DLR Supervisor	Institution or E-Mail	DLR Supervisor's ORCID iD
Thesis advisor	Kaur Betz, Pawandeep	pawandeep.kaur-betz (at) dlr.de	https://orcid.org/0000-0002-3073-326X
Thesis advisor	Fellegara, Riccardo	Riccardo.Fellegara (at) dlr.de	https://orcid.org/0000-0002-8758-2802

Date:

September 2025

Open Access:

Yes

Number of Pages:

153

Status:

Published

Keywords:

epidemiology, Gaussian Process Regression, modelling

Institution:

Georg-August-Universität Göttingen

HGF - Research field:

other

HGF - Program:

other

HGF - Program Themes:

other

DLR - Research area:

Digitalisation

DLR - Program:

D KIZ - Artificial Intelligence

DLR - Research theme (Project):

D - short study [KIZ]

Location:

Braunschweig

Institutes and Institutions:

Institute of Software Technology > Visual Computing and Engineering

Deposited By:

Kaur Betz, Pawandeep

Deposited On:

12 Dec 2025 08:38

Last Modified:

12 Dec 2025 08:38

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