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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Dezember 2020
Genre:	Schulbücher
	Computerhardware / graphicalmodelselections / MaximumClique / maximumspanningtrees / NetworkAnalysis / networkstructureidentification / Optimierung / randomvariablenetworks / Riskfunctionsfornetworkstructureidentification / robuststatisticalprocedures / statisticalproceduresfornetworkstructureidentification / Stochastik / stockmarketnetworks / Wahrscheinlichkeitsrechnung und Statistik mehr...
ISBN:	9783030602925
EAN-Code:	9783030602925
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 7 mm
Gewicht:	184 gr
Seiten:	112
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.