American Psychological Association 6th edition

Guignard, F. [ca. 2022]. On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory. In Springer Theses, Recognizing Outstanding Ph.D. Research (1 st ed. 2022) [Cd]. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-95231-0

ISO-690 (author-date, English)

GUIGNARD, Fabian, 2022. On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory. 1 st ed. 2022. Cham: Springer International Publishing. ISBN 9783030952310.

Modern Language Association 9th edition

Guignard, F. „On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory“. Springer Theses, Recognizing Outstanding Ph.D. Research, 1 st ed. 2022, cd, Springer International Publishing, 2022, https://doi.org/10.1007/978-3-030-95231-0.

Mohr Siebeck - Recht (Deutsch - Österreich)

Guignard, Fabian: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory, 1 st ed. 2022. Aufl. Cham 2022.

Emerald - Harvard

Guignard, F. (2022), On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory, Springer Theses, Recognizing Outstanding Ph.D. Research, 1 st ed. 2022., Bd. , Springer International Publishing, Cham, verfügbar unter:https://doi.org/10.1007/978-3-030-95231-0.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.