Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 139,81
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In den WarenkorbZustand: New. In.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 157,25
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In den WarenkorbHardcover. Zustand: Brand New. 411 pages. 9.25x6.50x1.25 inches. In Stock.
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. Pesents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. This title advocates and promotes a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. Editor(s): Dehmer, Matthias; Emmert-Streib, Frank; Mehler, Alexander. Num Pages: 395 pages, 114 black & white illustrations, 39 black & white tables, biography. BIC Classification: GPFC; PBT; PBW; TJK. Category: (P) Professional & Vocational. Dimension: 244 x 163 x 28. Weight in Grams: 740. . 2011. 2011th Edition. hardcover. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Birkhäuser Boston, Birkhäuser Boston, 2011
ISBN 10: 0817649034 ISBN 13: 9780817649036
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. As such, it marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines and can serve as a valuable resource for a diverse audience of advanced students and professional scientists. While it is primarily intended as a reference for research, the book could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.
Anbieter: Buchpark, Trebbin, Deutschland
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 412 | Sprache: Englisch | Produktart: Bücher | For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. As such, it marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines and can serve as a valuable resource for a diverse audience of advanced students and professional scientists. While it is primarily intended as a reference for research, the book could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.