American Psychological Association 6th edition

Eguchi, S., & Komori, O. [ca. 2022]. Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint (1 st ed. 2022) [Cd]. Tokyo: Springer Japan. https://doi.org/10.1007/978-4-431-56922-0

ISO-690 (author-date, English)

EGUCHI, Shinto und KOMORI, Osamu, 2022. Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint. 1 st ed. 2022. Tokyo: Springer Japan. ISBN 9784431569220.

Modern Language Association 9th edition

Eguchi, S., und O. Komori. Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint. 1 st ed. 2022, cd, Springer Japan, 2022, https://doi.org/10.1007/978-4-431-56922-0.

Mohr Siebeck - Recht (Deutsch - Österreich)

Eguchi, Shinto/Komori, Osamu: Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint, 1 st ed. 2022. Aufl. Tokyo 2022.

Emerald - Harvard

Eguchi, S. und Komori, O. (2022), Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint, 1 st ed. 2022., Bd. , Springer Japan, Tokyo, verfügbar unter:https://doi.org/10.1007/978-4-431-56922-0.

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