Sprache: Englisch
Verlag: Cambridge University Press, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
Anbieter: medimops, Berlin, Deutschland
Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
Sprache: Englisch
Verlag: Cambridge University Press, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 73,74
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 104,40
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 375 pages. 10.50x7.50x0.50 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
Anbieter: Kennys Bookstore, Olney, MD, USA
EUR 134,82
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 375 pages, 58 b/w illus. 5 tables. BIC Classification: PBT; PBWL; UMB; UTF. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 187 x 261 x 24. Weight in Grams: 800. . 2016. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Cambridge University Press, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models.