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oapen-20.500.12657-593812022-11-19T03:42:21Z Multivariate Statistical Analysis in the Real and Complex Domains Mathai, Arak M. Provost, Serge B. Haubold, Hans J. multivariate statistical analysis mathematical statistics complex domain matrix-variate Gaussian distributions Wishart distribution type-1 distributions type-2 distributions factor analysis classifications cluster profile analyses bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBW Applied mathematics bic Book Industry Communication::P Mathematics & science::PH Physics::PHS Statistical physics 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. 2022-11-18T14:20:57Z 2022-11-18T14:20:57Z 2022 book ONIX_20221118_9783030958640_49 9783030958640 https://library.oapen.org/handle/20.500.12657/59381 eng application/pdf Attribution 4.0 International 978-3-030-95864-0.pdf Springer Nature Springer International Publishing 10.1007/978-3-030-95864-0 10.1007/978-3-030-95864-0 6c6992af-b843-4f46-859c-f6e9998e40d5 9783030958640 Springer International Publishing 912 Cham open access
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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.
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