GP+: A Python library for kernel-based learning via Gaussian processes

In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions. GP+ is built on PyTorch and provides a user-friendly and object-ori...

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Bibliographische Detailangaben

Veröffentlicht in:	Advances in engineering software (1992) Jg. 195; S. 103686
Hauptverfasser:	Yousefpour, Amin, Foumani, Zahra Zanjani, Shishehbor, Mehdi, Mora, Carlos, Bostanabad, Ramin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.09.2024
Schlagworte:	Bayesian optimization Gaussian processes Kernel methods Manifold learning Python library Uncertainty quantification Uncertainty quantification Kernel methods Manifold learning Bayesian optimization Python library Gaussian processes
ISSN:	0965-9978
Online-Zugang:	Volltext
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