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Añadir al carritoPaperback. Condición: Brand New. 112 pages. 9.25x6.10x0.27 inches. In Stock.
Idioma: Inglés
Publicado por Springer International Publishing, Springer International Publishing Dez 2020, 2020
ISBN 10: 3030602923 ISBN 13: 9783030602925
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 112 pp. Englisch.
Idioma: Inglés
Publicado por Springer International Publishing, 2020
ISBN 10: 3030602923 ISBN 13: 9783030602925
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.
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Añadir al carritoTaschenbuch. Condición: Neu. Statistical Analysis of Graph Structures in Random Variable Networks | V. A. Kalyagin (u. a.) | Taschenbuch | SpringerBriefs in Optimization | viii | Englisch | 2020 | Springer | EAN 9783030602925 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Idioma: Inglés
Publicado por Springer International Publishing Dez 2020, 2020
ISBN 10: 3030602923 ISBN 13: 9783030602925
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful. 112 pp. Englisch.
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Idioma: Inglés
Publicado por Springer International Publishing, 2020
ISBN 10: 3030602923 ISBN 13: 9783030602925
Librería: moluna, Greven, Alemania
EUR 48,37
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical mo.