Titre :	Multivariate Statistical Analysis in the Real and Complex Domains
Type de document :	texte imprimé
Auteurs :	Mathai Arak M, Auteur
Année de publication :	2022
Importance :	1 vol. (921 p.)
Format :	24 cm
ISBN/ISSN/EAN :	978-3-030-95863-3
Langues :	Français (fre)
Catégories :	Mathématique
Mots-clés :	Mathématique
Index. décimale :	510 Mathématique
Résumé :	This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout.
Côte titre :	Fs/25065

Multivariate Statistical Analysis in the Real and Complex Domains [texte imprimé] / Mathai Arak M, Auteur . - 2022 . - 1 vol. (921 p.) ; 24 cm.
ISBN : 978-3-030-95863-3
Langues : Français (fre)

Catégories :	Mathématique
Mots-clés :	Mathématique
Index. décimale :	510 Mathématique
Résumé :	This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout.
Côte titre :	Fs/25065